Arrow Research search

Author name cluster

Liang Yang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

43 papers
2 author rows

Possible papers

43

AAAI Conference 2026 Conference Paper

Source-Free Graph Foundation Model Adaptation via Pseudo-Source Reconstruction

  • Liang Yang
  • Hui Ning
  • Jiaming Zhuo
  • Ziyi Ma
  • Chuan Wang
  • Wenning Wu
  • Zhen Wang

Aiming to overcome distribution shift and label sparsity that hinder cross-domain generalization of Graph Neural Networks (GNNs), Unsupervised Graph Domain Adaptation (UGDA) transfers knowledge from a label-rich source to an unlabeled target graph. Yet in practice, strict privacy protocols often withhold the source graph, reducing UGDA to the more constrained Source-Free UGDA (SFUGDA) where only a pre-trained source GNN remains. In this setting, the source GNN serves as a simple, task-specific graph foundation model. Despite recent progress, existing source-free UGDA methods remain hampered by source-knowledge absence: deprived of source graphs, they lose the reference distribution needed to gauge domain shift and must lean on noisy target cues, incurring biased adaptation and catastrophic forgetting. To overcome this drawback, this paper devises Source-Free Graph foundation model Adaptation via pseudo-source Reconstruction (SFGAR), a two-stage SFUGDA framework that first generates pseudo-source graphs to recover the source distribution encoded in a frozen pre-trained GNN, then adversarially aligns these synthetic graphs with the unlabeled target. Theoretical analysis shows that this proxy alignment tightly bounds the target-domain generalization error. Extensive experiments on public benchmarks validate the state-of-the-art performance of SFGAR.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Topology-aware Knowledge Preservation for Class-Incremental Learning

  • Han Zang
  • Yongfeng Dong
  • Linhao Li
  • Liang Yang
  • Yu Wang

Class Incremental Learning (CIL) aims to enable models to continually learn new classes while retaining previously learned knowledge. The principal challenge in CIL is catastrophic forgetting, which prior approaches typically address by distilling knowledge from previous model. However, such way is often limited to pairwise alignment, failing to preserve the underlying global manifold structure of feature space—ultimately resulting in semantic drift over time. To capture multi-scale structural patterns in the feature space, we propose a topology-aware distillation framework that leverages persistent homology. Specifically, by enforcing topological alignment across incremental stages, our method ensures structure-consistent knowledge transfer and robust preservation of old classes. Furthermore, we still devise a dual-branch architecture with an inverse sampling and dynamic reweighting mechanism that addresses the inherent data imbalance in standard replay-based frameworks. These innovations coalesce into TaKP (Topology-aware Knowledge Preservation), a unified framework designed to enhance knowledge preservation in CIL. Extensive experiments demonstrate that TaKP achieves state-of-the-art performance on multiple benchmarks, significantly improving old-class preservation and average accuracy.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

A Closer Look at Graph Transformers: Cross-Aggregation and Beyond

  • Jiaming Zhuo
  • Ziyi Ma
  • Yintong Lu
  • Yuwei Liu
  • Kun Fu
  • Di Jin
  • Chuan Wang
  • Wu Wenning

Graph Transformers (GTs), which effectively capture long-range dependencies and structural biases simultaneously, have recently emerged as promising alternatives to traditional Graph Neural Networks (GNNs). Advanced approaches for GTs to leverage topology information involve integrating GNN modules or modulating node attributes using positional encodings. Unfortunately, the underlying mechanism driving their effectiveness remains insufficiently understood. In this paper, we revisit these strategies and uncover a shared underlying mechanism—Cross Aggregation—that effectively captures the interaction between graph topology and node attributes. Building on this insight, we propose the Universal Graph Cross-attention Transformer (UGCFormer), a universal GT framework with linear computational complexity. The idea is to interactively learn the representations of graph topology and node attributes through a linearized Dual Cross-attention (DCA) module. In theory, this module can adaptively capture interactions between these two types of graph information, thereby achieving effective aggregation. To alleviate overfitting arising from the dual-channel design, we introduce a consistency constraint that enforces representational alignment. Extensive evaluations on multiple benchmark datasets demonstrate the effectiveness and efficiency of UGCFormer.

PDF Details

JBHI Journal 2025 Journal Article

A Knowledge-Guided Multi-modal Neural Network for Breast Cancer Molecular Subtyping

  • Jinlin Ye
  • Yuhan Liu
  • Shangjie Ren
  • Changjun Wang
  • Yidong Zhou
  • Liang Yang
  • Wei Zhang

Precise determination of HER2 subtype is essential for selecting appropriate targeted therapies in breast cancer. However, current HER2 assessment methods remain dependent on invasive tissue biopsies, which are limited by tumor heterogeneity and sampling bias. To address these challenges, this paper proposes a knowledge-guided multi-modal neural network (KMNet) for non-invasive HER2 subtyping by integrating clinical data and ultrasound images. KMNet introduces a Graph-based Clinical Feature encoder (GCF), which constructs a causal graph among clinical indicators based on medical knowledge and extracts high-order feature relationships via the Graph Convolutional Network (GCN). Meanwhile, the Convolutional Neural Network (CNN) and Vision Transformer (ViT)-based hybrid image encoder (CVUIF) captures both local details (calcifications and blood flow) and global dependencies between intra- and peritumoral regions. In addition, the Reduced Dimensional Fusion (RDF) module integrates key information from clinical graph features, ultrasound image features, and structured clinical data to construct a unified multi-modal representation for downstream HER2 subtyping task. Experiments were conducted on the private datasets (HER2USC) and the public datasets (BCW, BCa and SIIM-ISIC). Experimental results demonstrate that KMNet outperformed other reported state-ofthe- art multi-modal algorithms in HER2 subtyping task, offering strong potential for clinical decision support in breast cancer treatment.

Details DOI

AAAI Conference 2025 Conference Paper

Adaptive Decision Boundary for Few-Shot Class-Incremental Learning

  • Linhao Li
  • Yongzhang Tan
  • Siyuan Yang
  • Hao Cheng
  • Yongfeng Dong
  • Liang Yang

Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes from a limited set of training samples without forgetting knowledge of previously learned classes. Conventional FSCIL methods typically build a robust feature extractor during the base training session with abundant training samples and subsequently freeze this extractor, only fine-tuning the classifier in subsequent incremental phases. However, current strategies primarily focus on preventing catastrophic forgetting, considering only the relationship between novel and base classes, without paying attention to the specific decision spaces of each class. To address this challenge, we propose a plug-and-play Adaptive Decision Boundary Strategy (ADBS), which is compatible with most FSCIL methods. Specifically, we assign a specific decision boundary to each class and adaptively adjust these boundaries during training to optimally refine the decision spaces for the classes in each session. Furthermore, to amplify the distinctiveness between classes, we employ a novel inter-class constraint loss that optimizes the decision boundaries and prototypes for each class. Extensive experiments on three benchmarks, namely CIFAR100, miniImageNet, and CUB200, demonstrate that incorporating our ADBS method with existing FSCIL techniques significantly improves performance, achieving overall state-of-the-art results.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Attribute Association Driven Multi-Task Learning for Session-based Recommendation

  • Xinyao Wang
  • Zhizhi Yu
  • Dongxiao He
  • Liang Yang
  • Jianguo Wei
  • Di Jin

Session-based Recommendation (SBR) aims to predict users’ next interaction based on their current session without relying on long-term profiles. Despite its effectiveness in privacy-preserving and real-time scenarios, SBR remains challenging due to limited behavioral signals. Prior methods often overfit co-occurrence patterns, neglecting semantic priors like item attributes. Recent studies have attempted to incorporate item attributes (e. g. , category) by assigning fixed embeddings shared across all sessions. However, such approaches suffer from two key limitations: 1) Static attribute encoding fails to reflect semantic shifts under different session contexts. 2) Semantic misalignment between attribute and item ID embeddings. To address these issues, we propose attribute association driven multi-task learning for SBR, dubbed A²D-MTL. It explicitly models item categories using cross-session context to capture user potential interests and designs an adaptive sparse attention mechanism to suppress noise. Experimental results on three public datasets demonstrate the superiority of our method in recommendation accuracy (P@20) and ranking quality (MRR@20), validating the model’s effectiveness.

PDF Details DOI

JBHI Journal 2025 Journal Article

Evolving Dual-Directional Multiobjective Feature Selection for High-Dimensional Gene Expression Data

  • Yunhe Wang
  • Zhengyu Du
  • Xiaomin Li
  • Wenyuan Xiao
  • Hongpu Liu
  • Liang Yang

High-dimensional gene expression data has gained considerable attention in diverse medical fields such as disease diagnosis, with the challenges of the dimensionality curse and exponentially growing computation. To analyze the data, feature selection is an essential step by reducing the dimensionality. However, most feature selection algorithms for high-dimensional gene expression data still suffer from low classification and poor generalization ability. An evolutionary algorithm is an effective paradigm for enhancing global search capability in feature selection. Inspired by the evolutionary algorithm Competitive Swarm Optimization, we propose a Multiobjective Dual-directional Competitive Swarm Optimization (MODCSO) method for feature selection from high-dimensional gene expression data. First, we design a competitive swarm optimization algorithm framework based on multi-objective optimization to evolve three objective functions simultaneously. Then, we introduce a dual-directional learning strategy that trains particles within the loser group using two distinct learning strategies. To assess the effectiveness and efficiency of the suggested algorithm, we evaluate MODCSO through extensive experiments on twenty high-dimensional gene expression datasets and three real-world biological datasets. Compared to various leading feature selection algorithms, our proposed algorithm MODCSO exhibits superior competitiveness for the high-dimensional feature selection task. Moreover, we provide other extensive analyses to demonstrate further the robustness and biological interpretability of MODCSO in handling high-dimensional gene expression data.

Details DOI

AAAI Conference 2025 Conference Paper

Graph Contrastive Learning with Joint Spectral Augmentation of Attribute and Topology

  • Liang Yang
  • Zhenna Li
  • Jiaming Zhuo
  • Jing Liu
  • Ziyi Ma
  • Chuan Wang
  • Zhen Wang
  • Xiaochun Cao

As an essential technique for Graph Contrastive Learning (GCL), Graph Augmentation (GA) improves the generalization capability of the GCLs by introducing different forms of the same graph. To ensure information integrity, existing GA strategies have been designed to simultaneously process the two types of information available in graphs: node attributes and graph topology. Nonetheless, these strategies tend to augment the two types of graph information separately, ignoring their correlation, resulting in limited representation ability. To overcome this drawback, this paper proposes a novel GCL framework with a Joint spectrAl augMentation, named GCL-JAM. Motivated the equivalence between the graph learning objective on an attribute graph and the spectral clustering objective on the attribute-interpolated graph, the node attributes are first abstracted as another type of node to harmonize the node attributes and graph topology. The newly constructed graph is then utilized to perform spectral augmentation to capture the correlation during augmentation. Theoretically, the proposed joint spectral augmentation is proved to perturb more inter-class edges and noise attributes compared to separate augmentation methods. Extensive experiments on homophily and heterophily graphs validate the effectiveness and universality of GCL-JAM.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Semi-Supervised Clustering Framework for Fine-grained Scene Graph Generation

  • Jiarui Yang
  • Chuan Wang
  • Jun Zhang
  • Shuyi Wu
  • Jinjing Zhao
  • Zeming Liu
  • Liang Yang

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships existing in the scene. Considering the substantial human labor costs, existing scene graph annotations are often sparse and biased, which result in confusion training with low-frequency predicates. In this work, we design a Semi-Supervised Clustering framework for Scene Graph Generation (SSC-SGG) that uses the sparse labeled data to guide the generation of effective pseudo-labels from unlabeled object pairs, thus enriching the labeled sample space, especially for low-frequency interaction samples. We approach from the perspective of clustering, reducing the problem of confirmation bias in a self-training manner. Specifically, we first enhance the model's robustness to feature extraction via prototype-based clustering, aggregating different relationship augmented features onto the same prototype. Secondly, we design a dynamic pseudo-label assignment algorithm based on a mini-batch, which adjusts the detection sensitivity to different frequency samples from the historical assignment. Finally, we conduct joint training on the pseudo-labels and the labeled data. We conduct experiments on various SGG models and achieve substantial overall performance improvements, demonstrating the effectiveness of SSC-SGG.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Universal Graph Self-Contrastive Learning

  • Liang Yang
  • Yukun Cai
  • Hui Ning
  • Jiaming Zhuo
  • Di Jin
  • Ziyi Ma
  • Yuanfang Guo
  • Chuan Wang

As a pivotal architecture in Self-Supervised Learning (SSL), Graph Contrastive Learning (GCL) has demonstrated substantial application value in scenarios with limited labeled nodes (samples). However, existing GCLs encounter critical issues in the graph augmentation and positive and negative sampling stemming from the lack of explicit supervision, which collectively restrict their efficiency and universality. On the one hand, the reliance on graph augmentations in existing GCLs can lead to increased training times and memory usage, while potentially compromising the semantic integrity. On the other hand, the difficulty in selecting TRUE positive and negative samples for GCLs limits their universality to both homophilic and heterophilic graphs. To address these drawbacks, this paper introduces a novel GCL framework called GRAph learning via Self-contraSt (GRASS). The core mechanism is node-attribute self-contrast, which specifically involves increasing the feature similarities between nodes and their included attributes while decreasing the similarities between nodes and their non-included attributes. Theoretically, the self-contrast mechanism implicitly ensures accurate node-node contrast by capturing high-hop co-inclusion relationships, thereby enabling GRASS to be universally applicable to graphs with varying degrees of homophily. Evaluations on diverse benchmark datasets demonstrate the universality and efficiency of GRASS. The dataset and code are available at URL: https: //github. com/YukunCai/GRASS.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

SAM-Guided Masked Token Prediction for 3D Scene Understanding

  • Zhimin Chen
  • Liang Yang
  • Yingwei Li
  • Longlong Jing
  • Bing Li

Foundation models have significantly enhanced 2D task performance, and recent works like Bridge3D have successfully applied these models to improve 3D scene understanding through knowledge distillation, marking considerable advancements. Nonetheless, challenges such as the misalignment between 2D and 3D representations and the persistent long-tail distribution in 3D datasets still restrict the effectiveness of knowledge distillation from 2D to 3D using foundation models. To tackle these issues, we introduce a novel SAM-guided tokenization method that seamlessly aligns 3D transformer structures with region-level knowledge distillation, replacing the traditional KNN-based tokenization techniques. Additionally, we implement a group-balanced re-weighting strategy to effectively address the long-tail problem in knowledge distillation. Furthermore, inspired by the recent success of masked feature prediction, our framework incorporates a two-stage masked token prediction process in which the student model predicts both the global embeddings and token-wise local embeddings derived from the teacher models trained in the first stage. Our methodology has been validated across multiple datasets, including SUN RGB-D, ScanNet, and S3DIS, for tasks like 3D object detection and semantic segmentation. The results demonstrate significant improvements over current state-of-the-art self-supervised methods, establishing new benchmarks in this field.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Towards Comprehensive Detection of Chinese Harmful Memes

  • Junyu Lu
  • Bo Xu
  • Xiaokun Zhang
  • Hongbo Wang
  • Haohao Zhu
  • Dongyu Zhang
  • Liang Yang
  • Hongfei Lin

Harmful memes have proliferated on the Chinese Internet, while research on detecting Chinese harmful memes significantly lags behind due to the absence of reliable datasets and effective detectors. To this end, we present the comprehensive detection of Chinese harmful memes. We introduce ToxiCN MM, the first Chinese harmful meme dataset, which consists of 12, 000 samples with fine-grained annotations for meme types. Additionally, we propose a baseline detector, Multimodal Knowledge Enhancement (MKE), designed to incorporate contextual information from meme content, thereby enhancing the model's understanding of Chinese memes. In the evaluation phase, we conduct extensive quantitative experiments and qualitative analyses on multiple baselines, including LLMs and our MKE. Experimental results indicate that detecting Chinese harmful memes is challenging for existing models, while demonstrating the effectiveness of MKE.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Unified Graph Augmentations for Generalized Contrastive Learning on Graphs

  • Jiaming Zhuo
  • Yintong Lu
  • Hui Ning
  • Kun Fu
  • Bingxin Niu
  • Dongxiao He
  • Chuan Wang
  • Yuanfang Guo

In real-world scenarios, networks (graphs) and their tasks possess unique characteristics, requiring the development of a versatile graph augmentation (GA) to meet the varied demands of network analysis. Unfortunately, most Graph Contrastive Learning (GCL) frameworks are hampered by the specificity, complexity, and incompleteness of their GA techniques. Firstly, GAs designed for specific scenarios may compromise the universality of models if mishandled. Secondly, the process of identifying and generating optimal augmentations generally involves substantial computational overhead. Thirdly, the effectiveness of the GCL, even the learnable ones, is constrained by the finite selection of GAs available. To overcome the above limitations, this paper introduces a novel unified GA module dubbed UGA after reinterpreting the mechanism of GAs in GCLs from a message-passing perspective. Theoretically, this module is capable of unifying any explicit GAs, including node, edge, attribute, and subgraph augmentations. Based on the proposed UGA, a novel generalized GCL framework dubbed Graph cOntrastive UnifieD Augmentations (GOUDA) is proposed. It seamlessly integrates widely adopted contrastive losses and an introduced independence loss to fulfill the common requirements of consistency and diversity of augmentation across diverse scenarios. Evaluations across various datasets and tasks demonstrate the generality and efficiency of the proposed GOUDA over existing state-of-the-art GCLs.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Deepfake Video Detection via Facial Action Dependencies Estimation

  • Lingfeng Tan
  • Yunhong Wang
  • Junfu Wang
  • Liang Yang
  • Xunxun Chen
  • Yuanfang Guo

Deepfake video detection has drawn significant attention from researchers due to the security issues induced by deepfake videos. Unfortunately, most of the existing deepfake detection approaches have not competently modeled the natural structures and movements of human faces. In this paper, we formulate the deepfake video detection problem into a graph classification task, and propose a novel paradigm named Facial Action Dependencies Estimation (FADE) for deepfake video detection. We propose a Multi-Dependency Graph Module (MDGM) to capture abundant dependencies among facial action units, and extracts subtle clues in these dependencies. MDGM can be easily integrated into the existing frame-level detection schemes to provide significant performance gains. Extensive experiments demonstrate the superiority of our method against the state-of-the-art methods.

PDF Details DOI

ICRA Conference 2023 Conference Paper

FourStr: When Multi-sensor Fusion Meets Semi-supervised Learning

  • Bangquan Xie
  • Liang Yang
  • Zongming Yang
  • Ailin Wei
  • Xiaoxiong Weng
  • Bing Li 0008

This research proposes a novel semi-supervised learning framework FourStr (Four-Stream formed by two two-stream models) that focuses on the improvement of fusion and labeling efficiency for 3D multi-sensor detector. FourStr adopts a multi-sensor single-stage detector named adaptive fusion network (AFNet) as the backbone and trains it through the semi-supervision learning (SSL) strategy Stereo Fusion. Note that multi-sensor AFNet and SSL Stereo Fusion can benefit each other. On the one hand, the Four-stream composed of two AFNets naturally provides rich inputs and large models for SSL Stereo Fusion. While other SSL works have to use massive augmentation to obtain rich inputs, and deepen and widen the network for large models. On the other hand, by the novel three fusion stages and Loss Pruning, Stereo Fusion improves the fusion and labeling efficiency for AFNet. Finally, extensive experiments demonstrate that FourStr performs excellently on outdoor dataset (KITTI and Waymo Open Dataset) and indoor dataset (SUN RGB-D), especially for the small contour objects. And compared to the fully-supervised methods, FourStr achieves similar accuracy with only 2% labeled data on KITTI (or with 50% labeled data on SUN RGB-D).

Details

IJCAI Conference 2023 Conference Paper

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

  • Yuhan Chen
  • Yihong Luo
  • Jing Tang
  • Liang Yang
  • Siya Qiu
  • Chuan Wang
  • Xiaochun Cao

Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion of the information of multi-hop neighbors to include more nodes with homophily. However, the heterophily might differ among nodes, which requires to consider the local topology. Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module. For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. Moreover, we provide theoretical analysis on the effectiveness of LocalSim representing node homophily on synthetic graphs. Extensive evaluations over real benchmark datasets show that our proposed method, namely Local Similarity Graph Neural Network (LSGNN), can offer comparable or superior state-of-the-art performance on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model can significantly boost the performance of existing GNNs.

PDF Details DOI

NeurIPS Conference 2023 Conference Paper

Self-supervised Graph Neural Networks via Low-Rank Decomposition

  • Liang Yang
  • Runjie Shi
  • Qiuliang Zhang
  • Bingxin Niu
  • Zhen Wang
  • Xiaochun Cao
  • Chuan Wang

Self-supervised learning is introduced to train graph neural networks (GNNs) by employing propagation-based GNNs designed for semi-supervised learning tasks. Unfortunately, this common choice tends to cause two serious issues. Firstly, global parameters cause the model lack the ability to capture the local property. Secondly, it is difficult to handle networks beyond homophily without label information. This paper tends to break through the common choice of employing propagation-based GNNs, which aggregate representations of nodes belonging to different classes and tend to lose discriminative information. If the propagation in each ego-network is just between the nodes from the same class, the obtained representation matrix should follow the low-rank characteristic. To meet this requirement, this paper proposes the Low-Rank Decomposition-based GNNs (LRD-GNN-Matrix) by employing Low-Rank Decomposition to the attribute matrix. Furthermore, to incorporate long-distance information, Low-Rank Tensor Decomposition-based GNN (LRD-GNN-Tensor) is proposed by constructing the node attribute tensor from selected similar ego-networks and performing Low-Rank Tensor Decomposition. The employed tensor nuclear norm facilitates the capture of the long-distance relationship between original and selected similar ego-networks. Extensive experiments demonstrate the superior performance and the robustness of LRD-GNNs.

PDF Details

IROS Conference 2022 Conference Paper

FocusTR: Focusing on Valuable Feature by Multiple Transformers for Fusing Feature Pyramid on Object Detection

  • Bangquan Xie
  • Liang Yang
  • Zongming Yang
  • Ailin Wei
  • Xiaoxiong Weng
  • Bing Li 0008

The feature pyramid, which is a vital component of the convolutional neural networks, plays a significant role in several perception tasks, including object detection for autonomous driving. However, how to better fuse multi-level and multi-sensor feature pyramids is still a significant challenge, especially for object detection. This paper presents a FocusTR (Focusing on the valuable features by multiple Transformers), which is a simple yet effective architecture, to fuse feature pyramid for the single-stream 2D detector and two-stream 3D detector. Specifically, FocusTR encompasses several novel self-attention mechanisms, including the spatial-wise boxAlign attention (SB) for low-level spatial locations, context-wise affinity attention (CA) for high-level context information, and level-wise attention for the multi-level feature. To alleviate self-attention's computational complexity and slow training convergence, Fo-cusTR introduces a low and high-level fusion (LHF) to reduce the computational parameters, and the Pre- Ln [1]to accelerate the training convergence.

Details

NeurIPS Conference 2022 Conference Paper

OPEN: Orthogonal Propagation with Ego-Network Modeling

  • Liang Yang
  • Lina Kang
  • Qiuliang Zhang
  • Mengzhe Li
  • Bingxin Niu
  • Dongxiao He
  • Zhen Wang
  • Chuan Wang

To alleviate the unfavorable effect of noisy topology in Graph Neural networks (GNNs), some efforts perform the local topology refinement through the pairwise propagation weight learning and the multi-channel extension. Unfortunately, most of them suffer a common and fatal drawback: irrelevant propagation to one node and in multi-channels. These two kinds of irrelevances make propagation weights in multi-channels free to be determined by the labeled data, and thus the GNNs are exposed to overfitting. To tackle this issue, a novel Orthogonal Propagation with Ego-Network modeling (OPEN) is proposed by modeling relevances between propagations. Specifically, the relevance between propagations to one node is modeled by whole ego-network modeling, while the relevance between propagations in multi-channels is modeled via diversity requirement. By interpreting the propagations to one node from the perspective of dimension reduction, propagation weights are inferred from principal components of the ego-network, which are orthogonal to each other. Theoretical analysis and experimental evaluations reveal four attractive characteristics of OPEN as modeling high-order relationships beyond pairwise one, preventing overfitting, robustness, and high efficiency.

PDF Details

AAAI Conference 2022 Conference Paper

Self-Supervised Graph Neural Networks via Diverse and Interactive Message Passing

  • Liang Yang
  • Cheng Chen
  • Weixun Li
  • Bingxin Niu
  • Junhua Gu
  • Chuan Wang
  • Dongxiao He
  • Yuanfang Guo

By interpreting Graph Neural Networks (GNNs) as the message passing from the spatial perspective, their success is attributed to Laplacian smoothing. However, it also leads to serious over-smoothing issue by stacking many layers. Recently, many efforts have been paid to overcome this issue in semi-supervised learning. Unfortunately, it is more serious in unsupervised node representation learning task due to the lack of supervision information. Thus, most of the unsupervised or self-supervised GNNs often employ onelayer GCN as the encoder. Essentially, the over-smoothing issue is caused by the over-simplification of the existing message passing, which possesses two intrinsic limits: blind message and uniform passing. In this paper, a novel Diverse and Interactive Message Passing (DIMP) is proposed for selfsupervised learning by overcoming these limits. Firstly, to prevent the message from blindness and make it interactive between two connected nodes, the message is determined by both the two connected nodes instead of the attributes of one node. Secondly, to prevent the passing from uniformness and make it diverse over different attribute channels, different propagation weights are assigned to different elements in the message. To this end, a natural implementation of the message in DIMP is the element-wise product of the representations of two connected nodes. From the perspective of numerical optimization, the proposed DIMP is equivalent to performing an overlapping community detection via expectation-maximization (EM). Both the objective function of the community detection and the convergence of EM algorithm guarantee that DMIP can prevent from over-smoothing issue. Extensive evaluations on node-level and graph-level tasks demonstrate the superiority of DIMP on improving performance and overcoming over-smoothing issue.

PDF Details

NeurIPS Conference 2021 Conference Paper

Diverse Message Passing for Attribute with Heterophily

  • Liang Yang
  • Mengzhe Li
  • Liyang Liu
  • Bingxin Niu
  • Chuan Wang
  • Xiaochun Cao
  • Yuanfang Guo

Most of the existing GNNs can be modeled via the Uniform Message Passing framework. This framework considers all the attributes of each node in its entirety, shares the uniform propagation weights along each edge, and focuses on the uniform weight learning. The design of this framework possesses two prerequisites, the simplification of homophily and heterophily to the node-level property and the ignorance of attribute differences. Unfortunately, different attributes possess diverse characteristics. In this paper, the network homophily rate defined with respect to the node labels is extended to attribute homophily rate by taking the attributes as weak labels. Based on this attribute homophily rate, we propose a Diverse Message Passing (DMP) framework, which specifies every attribute propagation weight on each edge. Besides, we propose two specific strategies to significantly reduce the computational complexity of DMP to prevent the overfitting issue. By investigating the spectral characteristics, existing spectral GNNs are actually equivalent to a degenerated version of DMP. From the perspective of numerical optimization, we provide a theoretical analysis to demonstrate DMP's powerful representation ability and the ability of alleviating the over-smoothing issue. Evaluations on various real networks demonstrate the superiority of our DMP on handling the networks with heterophily and alleviating the over-smoothing issue, compared to the existing state-of-the-arts.

PDF Details

ICRA Conference 2021 Conference Paper

GPR-based Model Reconstruction System for Underground Utilities Using GPRNet

  • Jinglun Feng
  • Liang Yang
  • Ejup Hoxha
  • Diar Sanakov
  • Stanislav Sotnikov
  • Jizhong Xiao

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) instruments to detect and locate underground objects (i. e. rebars, utility pipes). Many of the previous researches focus on GPR image-based feature detection only, and none can process sparse GPR measurements to successfully reconstruct a very fine and detailed 3D model of underground objects for better visualization. To address this problem, this paper presents a novel robotic system to collect GPR data, localize the underground utilities, and reconstruct the underground objects’ dense point cloud model. This system is composed of three modules: 1) visual-inertial-based GPR data collection module which tags the GPR measurements with positioning information provided by an omnidirectional robot; 2) a deep neural network (DNN) migration module to interpret the raw GPR B-scan image into a cross-section of object model; 3) a DNN-based 3D reconstruction module, i. e. , GPRNet, to generate underground utility model with the fine 3D point cloud. In this paper, both the quantitative and qualitative experiment results verify our method that can generate a dense and complete point cloud model of pipe-shaped utilities based on a sparse input, i. e. , GPR raw data, with incompleteness and various noise. The experiment results on synthetic data as well as field test data further support the effectiveness of our approach.

Details

IJCAI Conference 2021 Conference Paper

Heterogeneous Graph Information Bottleneck

  • Liang Yang
  • Fan Wu
  • Zichen Zheng
  • Bingxin Niu
  • Junhua Gu
  • Chuan Wang
  • Xiaochun Cao
  • Yuanfang Guo

Most attempts on extending Graph Neural Networks (GNNs) to Heterogeneous Information Networks (HINs) implicitly take the direct assumption that the multiple homogeneous attributed networks induced by different meta-paths are complementary. The doubts about the hypothesis of complementary motivate an alternative assumption of consensus. That is, the aggregated node attributes shared by multiple homogeneous attributed networks are essential for node representations, while the specific ones in each homogeneous attributed network should be discarded. In this paper, a novel Heterogeneous Graph Information Bottleneck (HGIB) is proposed to implement the consensus hypothesis in an unsupervised manner. To this end, information bottleneck (IB) is extended to unsupervised representation learning by leveraging self-supervision strategy. Specifically, HGIB simultaneously maximizes the mutual information between one homogeneous network and the representation learned from another homogeneous network, while minimizes the mutual information between the specific information contained in one homogeneous network and the representation learned from this homogeneous network. Model analysis reveals that the two extreme cases of HGIB correspond to the supervised heterogeneous GNN and the infomax on homogeneous graph, respectively. Extensive experiments on real datasets demonstrate that the consensus-based unsupervised HGIB significantly outperforms most semi-supervised SOTA methods based on complementary assumption.

PDF Details DOI

AAAI Conference 2021 Conference Paper

Why Do Attributes Propagate in Graph Convolutional Neural Networks?

  • Liang Yang
  • Chuan Wang
  • Junhua Gu
  • Xiaochun Cao
  • Bingxin Niu

Many efforts have been paid to enhance Graph Convolutional Network from the perspective of propagation under the philosophy that “Propagation is the essence of the GCNNs”. Unfortunately, its adverse effect is over-smoothing, which makes the performance dramatically drop. To prevent the over-smoothing, many variants are presented. However, the perspective of propagation can’t provide an intuitive and unified interpretation to their effect on prevent over-smoothing. In this paper, we aim at providing a novel explanation to the question of “Why do attributes propagate in GCNNs? ”. which not only gives the essence of the oversmoothing, but also illustrates why the GCN extensions, including multi-scale GCN and GCN with initial residual, can improve the performance. To this end, an intuitive Graph Representation Learning (GRL) framework is presented. GRL simply constrains the node representation similar with the original attribute, and encourages the connected nodes possess similar representations (pairwise constraint). Based on the proposed GRL, exiting GCN and its extensions can be proved as different numerical optimization algorithms, such as gradient descent, of our proposed GRL framework. Inspired by the superiority of conjugate gradient descent compared to common gradient descent, a novel Graph Conjugate Convolutional (GCC) network is presented to approximate the solution to GRL with fast convergence. Specifically, GCC adopts the obtained information of the last layer, which can be represented as the difference between the input and output of the last layer, as the input to the next layer. Extensive experiments demonstrate the superior performance of GCC.

PDF Details

IJCAI Conference 2020 Conference Paper

Adversarial Mutual Information Learning for Network Embedding

  • Dongxiao He
  • Lu Zhai
  • Zhigang Li
  • Di Jin
  • Liang Yang
  • Yuxiao Huang
  • Philip S. Yu

Network embedding which is to learn a low dimensional representation of nodes in a network has been used in many network analysis tasks. Some network embedding methods, including those based on generative adversarial networks (GAN) (a promising deep learning technique), have been proposed recently. Existing GAN-based methods typically use GAN to learn a Gaussian distribution as a priori for network embedding. However, this strategy makes it difficult to distinguish the node representation from Gaussian distribution. Moreover, it does not make full use of the essential advantage of GAN (that is to adversarially learn the representation mechanism rather than the representation itself), leading to compromised performance of the method. To address this problem, we propose to use the adversarial idea on the representation mechanism, i. e. on the encoding mechanism under the framework of autoencoder. Specifically, we use the mutual information between node attributes and embedding as a reasonable alternative of this encoding mechanism (which is much easier to track). Additionally, we introduce another mapping mechanism (which is based on GAN) as a competitor into the adversarial learning system. A range of empirical results demonstrate the effectiveness of the proposed approach.

PDF Details DOI

ICRA Conference 2020 Conference Paper

GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet

  • Jinglun Feng
  • Liang Yang
  • Haiyan Wang 0019
  • Yifeng Song
  • Jizhong Xiao

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect the subsurface objects (i. e. rebars, utility pipes) and reveal the underground scene. One of the biggest challenges in GPR based inspection is the subsurface targets reconstruction. In order to address this issue, this paper presents a 3D GPR migration and dielectric prediction system to detect and reconstruct underground targets. This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i. e. , DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets. Our proposed DepthNet processes the GPR data by removing the noise in B-scan image as well as predicting depth of subsurface objects. For DepthNet model training and testing, we collect the real GPR data in the concrete test pit at Geophysical Survey System Inc. (GSSI) and create the synthetic GPR data by using gprMax3. 0 simulator. The dataset we create includes 350 labeled GPR images. The DepthNet achieves an average accuracy of 92. 64% for B-scan feature detection and an 0. 112 average error for underground target depth prediction. In addition, the experimental results verify that our proposed method improve the migration accuracy and performance in generating 3D GPR image compared with the traditional migration methods.

Details

IJCAI Conference 2020 Conference Paper

JANE: Jointly Adversarial Network Embedding

  • Liang Yang
  • Yuexue Wang
  • Junhua Gu
  • Chuan Wang
  • Xiaochun Cao
  • Yuanfang Guo

Motivated by the capability of Generative Adversarial Network on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module. The overall objective function of JANE is defined in a min-max form, which can be optimized via alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).

PDF Details DOI

AAAI Conference 2020 Conference Paper

Learning from Weak-Label Data: A Deep Forest Expedition

  • Qian-Wei Wang
  • Liang Yang
  • Yu-Feng Li

Weak-label learning deals with the problem where each training example is associated with multiple ground-truth labels simultaneously but only partially provided. This circumstance is frequently encountered when the number of classes is very large or when there exists a large ambiguity between class labels, and significantly influences the performance of multi-label learning. In this paper, we propose LCForest, which is the first tree ensemble based deep learning method for weak-label learning. Rather than formulating the problem as a regularized framework, we employ the recently proposed cascade forest structure, which processes information layerby-layer, and endow it with the ability of exploiting from weak-label data by a concise and highly efficient label complement structure. Specifically, in each layer, the label vector of each instance from testing-fold is modified with the predictions of random forests trained with the corresponding training-fold. Since the ground-truth label matrix is inaccessible, we can not estimate the performance via cross-validation directly. In order to control the growth of cascade forest, we adopt label frequency estimation and the complement flag mechanism. Experiments show that the proposed LCForest method compares favorably against the existing state-of-theart multi-label and weak-label learning methods.

PDF Details

ECAI Conference 2020 Conference Paper

Multi-Label Learning with Deep Forest

  • Liang Yang
  • Xi-Zhu Wu
  • Yuan Jiang 0001
  • Zhi-Hua Zhou

In multi-label learning, each instance is associated with multiple labels, and the crucial task is how to leverage label correlations in building models. The deep forest is a recent deep learning framework based on decision tree ensembles, which has a cascade structure that can do representation learning like deep neural models and does not rely on backpropagation. Though deep forests have been found useful in classification tasks, the potential of applying it into multi-label learning has not been studied. We consider that the layer-by-layer processing structure of the deep forest is appropriate for solving multi-label problems. Therefore we design the Multi-Label Deep Forest (MLDF) method, including two mechanisms: measure-aware feature reuse and measure-aware layer growth. The measure-aware feature reuse mechanism enables MLDF to reuse better representation in the previous layer. The measure-aware layer growth mechanism ensures MLDF gradually increase the model complexity guided by performance measure. MLDF handles two challenging problems at the same time: one is restricting the model complexity to ease the overfitting issue; another is optimizing the performance measure on user’s demand since there are many different measures in the multi-label evaluation. Experiments demonstrate that our proposal not only beats the compared methods over six measures on benchmark datasets but also enjoys label correlation discovery and other desired properties in multi-label learning.

Details

NeurIPS Conference 2019 Conference Paper

A Refined Margin Distribution Analysis for Forest Representation Learning

  • Shen-Huan Lyu
  • Liang Yang
  • Zhi-Hua Zhou

In this paper, we formulate the forest representation learning approach called \textsc{CasDF} as an additive model which boosts the augmented feature instead of the prediction. We substantially improve the upper bound of the generalization gap from $\mathcal{O}(\sqrt{\ln m/m})$ to $\mathcal{O}(\ln m/m)$, while the margin ratio of the margin standard deviation to the margin mean is sufficiently small. This tighter upper bound inspires us to optimize the ratio. Therefore, we design a margin distribution reweighting approach for deep forest to achieve a small margin ratio by boosting the augmented feature. Experiments confirm the correlation between the margin distribution and generalization performance. We remark that this study offers a novel understanding of \textsc{CasDF} from the perspective of the margin theory and further guides the layer-by-layer forest representation learning.

PDF Details

IROS Conference 2019 Conference Paper

Deep Neural Network based Visual Inspection with 3D Metric Measurement of Concrete Defects using Wall-climbing Robot

  • Liang Yang
  • Bing Li 0008
  • Guoyong Yang
  • Yong Chang
  • Zhaoming Liu
  • Biao Jiang
  • Jizhong Xiao

This paper presents a novel metric inspection robot system using a deep neural network to detect and measure surface flaws (i. e. , crack and spalling) on concrete structures performed by a wall-climbing robot. The system consists of four modules: robotics data collection module to obtain RGB-D images and IMU measurement, visual-inertial SLAM module to generate pose coupled key-frames with depth information, InspectionNet module to classify each pixel into three classes (back-ground, crack and spalling), and 3D registration and map fusion module to register the flaw patch into registered 3D model overlaid and highlighted with detected flaws for spatial-contextual visualization. The system enables the metric model of each surface flaw patch with pixel-level accuracy and determines its location in 3D space that is significant for structural health assessment and monitoring. The InspectionNet achieves an average accuracy of 87. 64% for crack and spalling inspection. We also demonstrate our InspectionNet is robust to view angle, scale and illumination variation. Finally, we design a metric voxel volume map to highlight the flaw in 3D model and provide location and metric information.

Details

IJCAI Conference 2019 Conference Paper

Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology

  • Liang Yang
  • Zhiyang Chen
  • Junhua Gu
  • Yuanfang Guo

The success of graph convolutional neural networks (GCNNs) based semi-supervised node classification is credited to the attribute smoothing (propagating) over the topology. However, the attributes may be interfered by the utilization of the topology information. This distortion will induce a certain amount of misclassifications of the nodes, which can be correctly predicted with only the attributes. By analyzing the impact of the edges in attribute propagations, the simple edges, which connect two nodes with similar attributes, should be given priority during the training process compared to the complex ones according to curriculum learning. To reduce the distortions induced by the topology while exploit more potentials of the attribute information, Dual Self-Paced Graph Convolutional Network (DSP-GCN) is proposed in this paper. Specifically, the unlabelled nodes with confidently predicted labels are gradually added into the training set in the node-level self-paced learning, while edges are gradually, from the simple edges to the complex ones, added into the graph during the training process in the edge-level self-paced learning. These two learning strategies are designed to mutually reinforce each other by coupling the selections of the edges and unlabelled nodes. Experimental results of transductive semi-supervised node classification on many real networks indicate that the proposed DSP-GCN has successfully reduced the attribute distortions induced by the topology while it gives superior performances with only one graph convolutional layer.

PDF Details

IJCAI Conference 2019 Conference Paper

Masked Graph Convolutional Network

  • Liang Yang
  • Fan Wu
  • Yingkui Wang
  • Junhua Gu
  • Yuanfang Guo

Semi-supervised classification is a fundamental technology to process the structured and unstructured data in machine learning field. The traditional attribute-graph based semi-supervised classification methods propagate labels over the graph which is usually constructed from the data features, while the graph convolutional neural networks smooth the node attributes, i. e. , propagate the attributes, over the real graph topology. In this paper, they are interpreted from the perspective of propagation, and accordingly categorized into symmetric and asymmetric propagation based methods. From the perspective of propagation, both the traditional and network based methods are propagating certain objects over the graph. However, different from the label propagation, the intuition ``the connected data samples tend to be similar in terms of the attributes", in attribute propagation is only partially valid. Therefore, a masked graph convolution network (Masked GCN) is proposed by only propagating a certain portion of the attributes to the neighbours according to a masking indicator, which is learned for each node by jointly considering the attribute distributions in local neighbourhoods and the impact on the classification results. Extensive experiments on transductive and inductive node classification tasks have demonstrated the superiority of the proposed method.

PDF Details

AAAI Conference 2019 Conference Paper

Orderly Subspace Clustering

  • Jing Wang
  • Atsushi Suzuki
  • Linchuan Xu
  • Feng Tian
  • Liang Yang
  • Kenji Yamanishi

Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure. Meanwhile, a reliable and easy-to-obtain supervision is desirable for practical learning. To meet these two objectives, in this paper we make the first attempt towards utilizing the orderly relationship, such as the data a is closer to b than to c, as a novel supervision. We propose an orderly subspace clustering approach with a novel regularization term. OSC enforces the learned representations to simultaneously capture the intrinsic subspace structure and reveal orderly structure that is faithful to true data relationship. Experimental results with several benchmarks have demonstrated that aside from more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer.

PDF Details

IJCAI Conference 2019 Conference Paper

Refining Word Representations by Manifold Learning

  • Chu Yonghe
  • Hongfei Lin
  • Liang Yang
  • Yufeng Diao
  • Shaowu Zhang
  • Fan Xiaochao

Pre-trained distributed word representations have been proven useful in various natural language processing (NLP) tasks. However, the effect of words’ geometric structure on word representations has not been carefully studied yet. The existing word representations methods underestimate the words whose distances are close in the Euclidean space, while overestimating words with a much greater distance. In this paper, we propose a word vector refinement model to correct the pre-trained word embedding, which brings the similarity of words in Euclidean space closer to word semantics by using manifold learning. This approach is theoretically founded in the metric recovery paradigm. Our word representations have been evaluated on a variety of lexical-level intrinsic tasks (semantic relatedness, semantic similarity) and the experimental results show that the proposed model outperforms several popular word representations approaches.

PDF Details

IJCAI Conference 2019 Conference Paper

Topology Optimization based Graph Convolutional Network

  • Liang Yang
  • Zesheng Kang
  • Xiaochun Cao
  • Di Jin
  • Bo Yang
  • Yuanfang Guo

In the past few years, semi-supervised node classification in attributed network has been developed rapidly. Inspired by the success of deep learning, researchers adopt the convolutional neural network to develop the Graph Convolutional Networks (GCN), and they have achieved surprising classification accuracy by considering the topological information and employing the fully connected network (FCN). However, the given network topology may also induce a performance degradation if it is directly employed in classification, because it may possess high sparsity and certain noises. Besides, the lack of learnable filters in GCN also limits the performance. In this paper, we propose a novel Topology Optimization based Graph Convolutional Networks (TO-GCN) to fully utilize the potential information by jointly refining the network topology and learning the parameters of the FCN. According to our derivations, TO-GCN is more flexible than GCN, in which the filters are fixed and only the classifier can be updated during the learning process. Extensive experiments on real attributed networks demonstrate the superiority of the proposed TO-GCN against the state-of-the-art approaches.

PDF Details

IJCAI Conference 2018 Conference Paper

3-in-1 Correlated Embedding via Adaptive Exploration of the Structure and Semantic Subspaces

  • Liang Yang
  • Yuanfang Guo
  • Di Jin
  • Huazhu Fu
  • Xiaochun Cao

Combinational network embedding, which learns the node representation by exploring both topological and non-topological information, becomes popular due to the fact that the two types of information are complementing each other. Most of the existing methods either consider the topological and non-topological information being aligned or possess predetermined preferences during the embedding process. Unfortunately, previous methods fail to either explicitly describe the correlations between topological and non-topological information or adaptively weight their impacts. To address the existing issues, three new assumptions are proposed to better describe the embedding space and its properties. With the proposed assumptions, nodes, communities and topics are mapped into one embedding space. A novel generative model is proposed to formulate the generation process of the network and content from the embeddings, with respect to the Bayesian framework. The proposed model automatically leans to the information which is more discriminative. The embedding result can be obtained by maximizing the posterior distribution by adopting the variational inference and reparameterization trick. Experimental results indicate that the proposed method gives superior performances compared to the state-of-the-art methods when a variety of real-world networks is analyzed.

PDF Details

IJCAI Conference 2018 Conference Paper

Integrative Network Embedding via Deep Joint Reconstruction

  • Di Jin
  • Meng Ge
  • Liang Yang
  • Dongxiao He
  • Longbiao Wang
  • Weixiong Zhang

Network embedding is to learn a low-dimensional representation for a network in order to capture intrinsic features of the network. It has been applied to many applications, e. g. , network community detection and user recommendation. One of the recent research topics for network embedding has been focusing on exploitation of diverse information, including network topology and semantic information on nodes of networks. However, such diverse information has not been fully utilized nor adequately integrated in the existing methods, so that the resulting network embedding is far from satisfactory. In this paper, we develop a weight-free multi-component network embedding approach by network reconstruction via a deep Autoencoder. Three key components make our new approach effective, i. e. , a uniformed graph representation of network topology and semantic information, enhancement to the graph representation using local network structure (i. e. , pairwise relationship on nodes) by sampling with latent space regularization, and integration of the diverse information in graph forms in a deep Autoencoder. Extensive experimental results on seven real-world networks demonstrate a superior performance of our method over nine state-of-the-art methods for embedding.

PDF Details

AAAI Conference 2018 Conference Paper

Multi-Facet Network Embedding: Beyond the General Solution of Detection and Representation

  • Liang Yang
  • Yuanfang Guo
  • Xiaochun Cao

In network analysis, community detection and network embedding are two important topics. Community detection tends to obtain the most noticeable partition, while network embedding aims at seeking node representations which contains as many diverse properties as possible. We observe that the current community detection and network embedding problems are being resolved by a general solution, i. e. , “maximizing the consistency between similar nodes while maximizing the distance between the dissimilar nodes”. This general solution only exploits the most noticeable structure (facet) of the network, which effectively satisfies the demands of the community detection. Unfortunately, most of the specific embedding algorithms, which are developed from the general solution, cannot achieve the goal of network embedding by exploring only one facet of the network. To improve the general solution for better modeling the real network, we propose a novel network embedding method, Multi-facet Network Embedding (MNE), to capture the multiple facets of the network. MNE learns multiple embeddings simultaneously, with the Hilbert Schmidt Independence Criterion (HSIC) being the a diversity constraint. To efficiently solve the optimization problem, we propose a Binary HSIC with linear complexity and solve the MNE objective function by adopting the Augmented Lagrange Multiplier (ALM) method. The overall complexity is linear with the scale of the network. Extensive results demonstrate that MNE gives efficient performances and outperforms the state-of-the-art network embedding methods.

PDF Details

IJCAI Conference 2017 Conference Paper

Multi-Component Nonnegative Matrix Factorization

  • Jing Wang
  • Feng Tian
  • Xiao Wang
  • Hongchuan Yu
  • Chang Hong Liu
  • Liang Yang

Real data are usually complex and contain various components. For example, face images have expressions and genders. Each component mainly reflects one aspect of data and provides information others do not have. Therefore, exploring the semantic information of multiple components as well as the diversity among them is of great benefit to understand data comprehensively and in-depth. However, this cannot be achieved by current nonnegative matrix factorization (NMF)-based methods, despite that NMF has shown remarkable competitiveness in learning parts-based representation of data. To overcome this limitation, we propose a novel multi-component nonnegative matrix factorization (MCNMF). Instead of seeking for only one representation of data, MCNMF learns multiple representations simultaneously, with the help of the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term. HSIC explores the diverse information among the representations, where each representation corresponds to a component. By integrating the multiple representations, a more comprehensive representation is then established. A new iterative updating optimization scheme is derived to solve the objective function of MCNMF, along with its correctness and convergence guarantees. Extensive experimental results on real-world datasets have shown that MCNMF not only achieves more accurate performance over the state-of-the-arts using the aggregated representation, but also interprets data from different aspects with the multiple representations, which is beyond what current NMFs can offer.

PDF Details

IJCAI Conference 2016 Conference Paper

Modularity Based Community Detection with Deep Learning

  • Liang Yang
  • Xiaochun Cao
  • Dongxiao He
  • Chuan Wang
  • Xiao Wang
  • Weixiong Zhang

Identification of module or community structures is important for characterizing and understanding complex systems. While designed with different objectives, i. e. , stochastic models for regeneration and modularity maximization models for discrimination, both these two types of model look for low-rank embedding to best represent and reconstruct network topology. However, the mapping through such embedding is linear, whereas real networks have various nonlinear features, making these models less effective in practice. Inspired by the strong representation power of deep neural networks, we propose a novel nonlinear reconstruction method by adopting deep neural networks for representation. We then extend the method to a semi-supervised community detection algorithm by incorporating pairwise constraints among graph nodes. Extensive experimental results on synthetic and real networks show that the new methods are effective, outperforming most state-of-the-art methods for community detection.

PDF Details

AAAI Conference 2016 Conference Paper

Semantic Community Identification in Large Attribute Networks

  • Xiao Wang
  • Di Jin
  • Xiaochun Cao
  • Liang Yang
  • Weixiong Zhang

Identification of modular or community structures of a network is a key to understanding the semantics and functions of the network. While many network community detection methods have been developed, which primarily explore network topologies, they provide little semantic information of the communities discovered. Although structures and semantics are closely related, little effort has been made to discover and analyze these two essential network properties together. By integrating network topology and semantic information on nodes, e. g. , node attributes, we study the problems of detection of communities and inference of their semantics simultaneously. We propose a novel nonnegative matrix factorization (NMF) model with two sets of parameters, the community membership matrix and community attribute matrix, and present efficient updating rules to evaluate the parameters with a convergence guarantee. The use of node attributes improves upon community detection and provides a semantic interpretation to the resultant network communities. Extensive experimental results on synthetic and real-world networks not only show the superior performance of the new method over the state-of-the-art approaches, but also demonstrate its ability to semantically annotate the communities.

PDF Details

IROS Conference 2015 Conference Paper

Generation of dynamically feasible and collision free trajectory by applying six-order Bezier curve and local optimal reshaping

  • Liang Yang
  • Dalei Song
  • Jizhong Xiao
  • Jianda Han
  • Liying Yang 0002
  • Yang Cao

This paper considers the problem of generating dynamically feasible and collision free trajectory for unmanned aerial vehicles(UAVs) in cluttered environments. General random-based searching algorithms output piecewise linear paths, which cause big discrepancy when used as navigation reference for UAVs with high speed. Meanwhile, the disturbance may also occur to lead the UAVs into danger. In order to obtain agile autonomy without potential dangers, this paper introduces a three-step method to generate feasible reference. In the first step, a six-order Bezier curve, which uses Tuning Rotation to decrease the curvature, is introduced to smooth the output of the path planner. Then a forward simulation is implemented to find the potential dangerous regions. Finally, the path is reshaped by local optimal reshaping planner to eliminate residual dangers. The three steps form a circulation, the reshaped path sent to the first step again to check dynamic feasibility and safety. The method combining Six-order Bezier curve, Tuning Rotation, and local optimal reshaping is proposed by us for the first time, where the Tuning Rotation is able to meet various curvature requirements without violating the previous path, local optimal reshaping obtains both temporal and spatial reshaping with high time efficiency. The method addresses the system dynamics to achieve agile autonomy, which provides the geometry reference as well as the low level control. The effectiveness of the proposed method is demonstrated by the simulations.

Details