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Yanfang Ye

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30

AAAI Conference 2026 System Paper

3D4D: An Interactive, Editable, 4D World Model via 3D Video Generation

  • Yunhong He
  • Zhengqing Yuan
  • Zhengzhong Tu
  • Yanfang Ye
  • Lichao Sun

We introduce DreamLand, an interactive 4D visualization framework that integrates WebGL with Supersplat rendering. It transforms static images and text into coherent 4D scenes through four core modules and employs a foveated rendering strategy for efficient, real-time multi-modal interaction. This framework enables adaptive, user-driven exploration of complex 4D environments.

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AAAI Conference 2026 Conference Paper

Vision-MoR: Scaling Vision Transformer via Patch-Level Mixture-of-Recursions

  • Yunhong He
  • Zhengqing Yuan
  • Weixiang Sun
  • YiYang Li
  • Yixin Liu
  • Yanfang Ye
  • Lichao Sun

Scaling Vision Transformers (ViTs) has yielded remarkable advancements in diverse vision tasks, albeit at the cost of escalating computational, memory, and parameter demands. Existing efficiency techniques typically address only one dimension, computation, memory, or parameters, lacking a cohesive approach. In this paper, we introduce Vision-MoR, a novel ViT architecture that unifies parameter sharing, spatially adaptive computation, and memory-efficient design into a single framework. Vision-MoR employs a spatial-aware router with shifted-window attention to dynamically assign per-patch recursion depths, coupled with a recursive Transformer loop enabling token-wise early exiting. This facilitates content-adaptive processing and recursive parameter reuse while preserving spatial locality. On ImageNet-1K, Vision-MoR Small attains 74.6% Top-1 accuracy with 140M FLOPs and 5.7M parameters, outperforming EfficientViT-M2 (70.8%) and SHViT-S1 (72.8%) at superior throughput. The Vision-MoR X-Large variant achieves 80.4% Top-1 and 95.2% Top-5 accuracy using 14.3M parameters and 2044M FLOPs, surpassing ResNet-50 and EfficientNet-B1. On COCO object detection, Vision-MoR X-Large yields 39.1 AP with the lowest latency among comparable models. These results underscore Vision-MoR's state-of-the-art accuracy-efficiency trade-offs, positioning it as a scalable, deployment-friendly backbone for real-time vision applications.

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NeurIPS Conference 2025 Conference Paper

AutoData: A Multi-Agent System for Open Web Data Collection

  • Tianyi Ma
  • Yiyue Qian
  • Zheyuan Zhang
  • Zehong Wang
  • Xiaoye Qian
  • Feifan Bai
  • Yifan Ding
  • Xuwei Luo

The exponential growth of data-driven systems and AI technologies has intensified the demand for high-quality web-sourced datasets. While existing datasets have proven valuable, conventional web data collection approaches face significant limitations in terms of human effort and scalability. Current data collecting solutions fall into two categories: wrapper-based methods that struggle with adaptability and reproducibility, and large language model (LLM)-based approaches that incur substantial computational and financial costs. To address these challenges, we propose AutoData, a novel multi-agent system for Automated web Data collection, that requires minimal human intervention, i. e. , only necessitating a natural language instruction specifying the desired dataset. In addition, AutoData is designed for a robust multi-agent architecture, featuring a novel oriented message hypergraph coordinated by a central task manager, to efficiently organize agents across research and development squads. Besides, we introduce a novel hypergraph cache system to advance the multi-agent collaboration process that enables efficient automated data collection and mitigates the token cost issues prevalent in existing LLM-based systems. Moreover, we introduce Instruct2DS, a new benchmark dataset supporting live data collection from web sources across three domains: academic, finance, and sports. Comprehensive evaluations over Instruct2DS and three existing benchmark datasets demonstrate AutoData's superior performance compared to baseline methods. Case studies on challenging tasks such as picture book collection and paper extraction from surveys further validate its applicability.

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AAAI Conference 2025 Conference Paper

AutoFEA: Enhancing AI Copilot by Integrating Finite Element Analysis Using Large Language Models with Graph Neural Networks

  • Shifu Hou
  • Rick Johnson
  • Ramandeep Makhija
  • Lingwei Chen
  • Yanfang Ye

Large Language Models (LLMs) have demonstrated significant potential across various applications, but their use as AI copilots in complex and specialized tasks is often hindered by AI hallucinations, where models generate outputs that seem plausible but are incorrect. To address this challenge, we develop AutoFEA, an intelligent system that integrates LLMs with Finite Element Analysis (FEA) to automate the generation of FEA input files. Our approach features a novel planning method and a graph convolutional network (GCN)-Transformer Link Prediction retrieval model, which enhances the accuracy and reliability of the generated simulations. The AutoFEA system proceeds with key steps: dataset preparation, step-by-step planning, GCN-Transformer Link Prediction retrieval, LLM-driven code generation, and simulation using CalculiX. In this workflow, the GCN-Transformer model predicts and retrieves relevant example codes based on relationships between different steps in the FEA process, guiding the LLM in generating accurate simulation codes. We validate AutoFEA using a specialized dataset of 512 meticulously prepared FEA projects, which provides a robust foundation for training and evaluation. Our results demonstrate that AutoFEA significantly reduces AI hallucinations by grounding LLM outputs in physically accurate simulation data, thereby improving the success rate and accuracy of FEA simulations and paving the way for future advancements in AI-assisted engineering tasks.

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NeurIPS Conference 2025 Conference Paper

Generative Graph Pattern Machine

  • Zehong Wang
  • Zheyuan Zhang
  • Tianyi Ma
  • Chuxu Zhang
  • Yanfang Ye

Graph neural networks (GNNs) have been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their success, message-passing suffers from fundamental limitations---including constrained expressiveness, over-smoothing, over-squashing, and limited capacity to model long-range dependencies. These issues hinder scalability: increasing data size or model size often fails to yield improved performance. To this end, we explore pathways beyond message-passing and introduce Generative Graph Pattern Machine (G$^2$PM), a generative Transformer pre-training framework for graphs. G$^2$PM represents graph instances (nodes, edges, or entire graphs) as sequences of substructures, and employs generative pre-training over the sequences to learn generalizable and transferable representations. Empirically, G$^2$PM demonstrates strong scalability: on the ogbn-arxiv benchmark, it continues to improve with model sizes up to 60M parameters, outperforming prior generative approaches that plateau at significantly smaller scales (e. g. , 3M). In addition, we systematically analyze the model design space, highlighting key architectural choices that contribute to its scalability and generalization. Across diverse tasks---including node/link/graph classification, transfer learning, and cross-graph pretraining---G$^2$PM consistently outperforms strong baselines, establishing a compelling foundation for scalable graph learning. The code and dataset are available at https: //github. com/Zehong-Wang/G2PM.

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NeurIPS Conference 2024 Conference Paper

GFT: Graph Foundation Model with Transferable Tree Vocabulary

  • Zehong Wang
  • Zheyuan Zhang
  • Nitesh V. Chawla
  • Chuxu Zhang
  • Yanfang Ye

Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in the areas such as scientific research, social network analysis, drug discovery, and e-commerce. Despite the significant progress of pre-trained graph neural networks, there haven’t been GFMs that can achieve desired performance on various graph-learning-related tasks. Building GFMs may rely on a vocabulary that encodes transferable patterns shared among different tasks and domains. Unlike image and text, defining such transferable patterns for graphs remains an open question. In this paper, we aim to bridge this gap by rethinking the transferable patterns on graphs as computation trees -- i. e. , tree structures derived from the message-passing process. Based on this insight, we propose a cross-task, cross-domain graph foundation model named GFT, short for Graph Foundation model with transferable Tree vocabulary. By treating computation trees as tokens within the transferable vocabulary, GFT improves model generalization and reduces the risk of negative transfer. The theoretical analyses and extensive experimental studies have demonstrated the transferability of computation trees and shown the effectiveness of GFT across diverse tasks and domains in graph learning. The open source code and data are available at https: //github. com/Zehong-Wang/GFT.

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NeurIPS Conference 2024 Conference Paper

How Does Message Passing Improve Collaborative Filtering?

  • Clark M. Ju
  • William Shiao
  • Zhichun Guo
  • Yanfang Ye
  • Yozen Liu
  • Neil Shah
  • Tong Zhao

Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods by message passing (MP) used in graph neural networks, due to its strong capabilities of extracting knowledge from graph-structured data, like user-item bipartite graphs that naturally exist in CF. They assume that MP helps CF methods in a manner akin to its benefits for graph-based learning tasks in general (e. g. , node classification). However, even though MP empirically improves CF, whether or not this assumption is correct still needs verification. To address this gap, we formally investigate why MP helps CF from multiple perspectives and show that many assumptions made by previous works are not entirely accurate. With our curated ablation studies and theoretical analyses, we discover that (i) MP improves the CF performance primarily by additional representations passed from neighbors during the forward pass instead of additional gradient updates to neighbor representations during the model back-propagation and (ii) MP usually helps low-degree nodes more than high-degree nodes. }Utilizing these novel findings, we present Test-time Aggregation for Collaborative Filtering, namely TAG-CF, a test-time augmentation framework that only conducts MP once at inference time. The key novelty of TAG-CF is that it effectively utilizes graph knowledge while circumventing most of notorious computational overheads of MP. Besides, TAG-CF is extremely versatile can be used as a plug-and-play module to enhance representations trained by different CF supervision signals. Evaluated on six datasets (i. e. , five academic benchmarks and one real-world industrial dataset), TAG-CF consistently improves the recommendation performance of CF methods without graph by up to 39. 2% on cold users and 31. 7% on all users, with little to no extra computational overheads. Furthermore, compared with trending graph-enhanced CF methods, TAG-CF delivers comparable or even better performance with less than 1% of their total training times. Our code is publicly available at https: //github. com/snap-research/Test-time-Aggregation-for-CF.

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IJCAI Conference 2024 Conference Paper

Subgraph Pooling: Tackling Negative Transfer on Graphs

  • Zehong Wang
  • Zheyuan Zhang
  • Chuxu Zhang
  • Yanfang Ye

Transfer learning aims to enhance performance on a target task by using knowledge from related tasks. However, when the source and target tasks are not closely aligned, it can lead to reduced performance, known as negative transfer. Unlike in image or text data, we find that negative transfer could commonly occur in graph-structured data, even when source and target graphs have semantic similarities. Specifically, we identify that structural differences significantly amplify the dissimilarities in the node embeddings across graphs. To mitigate this, we bring a new insight in this paper: for semantically similar graphs, although structural differences lead to significant distribution shift in node embeddings, their impact on subgraph embeddings could be marginal. Building on this insight, we introduce Subgraph Pooling (SP) by aggregating nodes sampled from a k-hop neighborhood and Subgraph Pooling++ (SP++) by a random walk, to mitigate the impact of graph structural differences on knowledge transfer. We theoretically analyze the role of SP in reducing graph discrepancy and conduct extensive experiments to evaluate its superiority under various settings. The proposed SP methods are effective yet elegant, which can be easily applied on top of any backbone Graph Neural Networks (GNNs). Our code and data are available at: https: //github. com/Zehong-Wang/Subgraph-Pooling.

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NeurIPS Conference 2023 Conference Paper

GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation

  • Mingxuan Ju
  • Tong Zhao
  • Wenhao Yu
  • Neil Shah
  • Yanfang Ye

Recent studies have shown that graph neural networks (GNNs) exhibit strong biases towards the node degree: they usually perform satisfactorily on high-degree nodes with rich neighbor information but struggle with low-degree nodes. Existing works tackle this problem by deriving either designated GNN architectures or training strategies specifically for low-degree nodes. Though effective, these approaches unintentionally create an artificial out-of-distribution scenario, where models mainly or even only observe low-degree nodes during the training, leading to a downgraded performance for high-degree nodes that GNNs originally perform well at. In light of this, we propose a test-time augmentation framework, namely GraphPatcher, to enhance test-time generalization of any GNNs on low-degree nodes. Specifically, GraphPatcher iteratively generates virtual nodes to patch artificially created low-degree nodes via corruptions, aiming at progressively reconstructing target GNN's predictions over a sequence of increasingly corrupted nodes. Through this scheme, GraphPatcher not only learns how to enhance low-degree nodes (when the neighborhoods are heavily corrupted) but also preserves the original superior performance of GNNs on high-degree nodes (when lightly corrupted). Additionally, GraphPatcher is model-agnostic and can also mitigate the degree bias for either self-supervised or supervised GNNs. Comprehensive experiments are conducted over seven benchmark datasets and GraphPatcher consistently enhances common GNNs' overall performance by up to 3. 6% and low-degree performance by up to 6. 5%, significantly outperforming state-of-the-art baselines. The source code is publicly available at https: //github. com/jumxglhf/GraphPatcher.

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AAAI Conference 2023 Conference Paper

Let Graph Be the Go Board: Gradient-Free Node Injection Attack for Graph Neural Networks via Reinforcement Learning

  • Mingxuan Ju
  • Yujie Fan
  • Chuxu Zhang
  • Yanfang Ye

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security standards, such as product recommendation and user behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and further downgrading its performance become extremely incentive for adversaries. Previous attackers mainly focus on structural perturbations or node injections to the existing graphs, guided by gradients from the surrogate models. Although they deliver promising results, several limitations still exist. For the structural perturbation attack, to launch a proposed attack, adversaries need to manipulate the existing graph topology, which is impractical in most circumstances. Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model. To bridge these gaps, in this paper, we study the problem of black-box node injection attack, without training a potentially misleading surrogate model. Specifically, we model the node injection attack as a Markov decision process and propose Gradient-free Graph Advantage Actor Critic, namely G2A2C, a reinforcement learning framework in the fashion of advantage actor critic. By directly querying the victim model, G2A2C learns to inject highly malicious nodes with extremely limited attacking budgets, while maintaining a similar node feature distribution. Through our comprehensive experiments over eight acknowledged benchmark datasets with different characteristics, we demonstrate the superior performance of our proposed G2A2C over the existing state-of-the-art attackers. Source code is publicly available at: https://github.com/jumxglhf/G2A2C.

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TMLR Journal 2023 Journal Article

Mind the Gap: Mitigating the Distribution Gap in Graph Few-shot Learning

  • Chunhui Zhang
  • Hongfu Liu
  • Jundong Li
  • Yanfang Ye
  • Chuxu Zhang

Prevailing supervised deep graph learning models often suffer from the issue of label scarcity, leading to performance degradation in the face of limited annotated data. Although numerous graph few-shot learning (GFL) methods have been developed to mitigate this problem, they tend to rely excessively on labeled data. This over-reliance on labeled data can result in impaired generalization ability in the test phase due to the existence of a distribution gap. Moreover, existing GFL methods lack a general purpose as their designs are coupled with task or data-specific characteristics. To address these shortcomings, we propose a novel Self-Distilled Graph Few-shot Learning framework (SDGFL) that is both general and effective. SDGFL leverages a self-distilled contrastive learning procedure to boost GFL. Specifically, our model first pre-trains a graph encoder with contrastive learning using unlabeled data. Later, the trained encoder is frozen as a teacher model to distill a student model with a contrastive loss. The distilled model is then fed to GFL. By learning data representation in a self-supervised manner, SDGFL effectively mitigates the distribution gap and enhances generalization ability. Furthermore, our proposed framework is task and data-independent, making it a versatile tool for general graph mining purposes. To evaluate the effectiveness of our proposed framework, we introduce an information-based measurement that quantifies its capability. Through comprehensive experiments, we demonstrate that SDGFL outperforms state-of-the-art baselines on various graph mining tasks across multiple datasets in the few-shot scenario. We also provide a quantitative measurement of SDGFL’s superior performance in comparison to existing methods.

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AAAI Conference 2022 Conference Paper

Adaptive Kernel Graph Neural Network

  • Mingxuan Ju
  • Shifu Hou
  • Yujie Fan
  • Jianan Zhao
  • Yanfang Ye
  • Liang Zhao

Graph neural networks (GNNs) have demonstrated great success in representation learning for graph-structured data. The layer-wise graph convolution in GNNs is shown to be powerful at capturing graph topology. During this process, GNNs are usually guided by pre-defined kernels such as Laplacian matrix, adjacency matrix, or their variants. However, the adoptions of pre-defined kernels may restrain the generalities to different graphs: mismatch between graph and kernel would entail sub-optimal performance. For example, GNNs that focus on low-frequency information may not achieve satisfactory performance when high-frequency information is significant for the graphs, and vice versa. To solve this problem, in this paper, we propose a novel framework - i. e. , namely Adaptive Kernel Graph Neural Network (AKGNN) which learns to adapt to the optimal graph kernel in a unified manner at the first attempt. In the proposed AKGNN, we first design a data-driven graph kernel learning mechanism, which adaptively modulates the balance between all-pass and lowpass filters by modifying the maximal eigenvalue of the graph Laplacian. Through this process, AKGNN learns the optimal threshold between high and low frequency signals to relieve the generality problem. Later, we further reduce the number of parameters by a parameterization trick and enhance the expressive power by a global readout function. Extensive experiments are conducted on acknowledged benchmark datasets and promising results demonstrate the outstanding performance of our proposed AKGNN by comparison with state-of-the-art GNNs. The source code is publicly available at: https: //github. com/jumxglhf/AKGNN.

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NeurIPS Conference 2022 Conference Paper

Co-Modality Graph Contrastive Learning for Imbalanced Node Classification

  • Yiyue Qian
  • Chunhui Zhang
  • Yiming Zhang
  • Qianlong Wen
  • Yanfang Ye
  • Chuxu Zhang

Graph contrastive learning (GCL), leveraging graph augmentations to convert graphs into different views and further train graph neural networks (GNNs), has achieved considerable success on graph benchmark datasets. Yet, there are still some gaps in directly applying existing GCL methods to real-world data. First, handcrafted graph augmentations require trials and errors, but still can not yield consistent performance on multiple tasks. Second, most real-world graph data present class-imbalanced distribution but existing GCL methods are not immune to data imbalance. Therefore, this work proposes to explicitly tackle these challenges, via a principled framework called \textit{\textbf{C}o-\textbf{M}odality \textbf{G}raph \textbf{C}ontrastive \textbf{L}earning} (\textbf{CM-GCL}) to automatically generate contrastive pairs and further learn balanced representation over unlabeled data. Specifically, we design inter-modality GCL to automatically generate contrastive pairs (e. g. , node-text) based on rich node content. Inspired by the fact that minority samples can be ``forgotten'' by pruning deep neural networks, we naturally extend network pruning to our GCL framework for mining minority nodes. Based on this, we co-train two pruned encoders (e. g. , GNN and text encoder) in different modalities by pushing the corresponding node-text pairs together and the irrelevant node-text pairs away. Meanwhile, we propose intra-modality GCL by co-training non-pruned GNN and pruned GNN, to ensure node embeddings with similar attribute features stay closed. Last, we fine-tune the GNN encoder on downstream class-imbalanced node classification tasks. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art baseline models and learns more balanced representations on real-world graphs. Our source code is available at https: //github. com/graphprojects/CM-GCL.

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AAAI Conference 2022 Conference Paper

Disentangled Spatiotemporal Graph Generative Models

  • Yuanqi Du
  • Xiaojie Guo
  • Hengning Cao
  • Yanfang Ye
  • Liang Zhao

Spatiotemporal graph represents a crucial data structure where the nodes and edges are embedded in a geometric space and can evolve dynamically over time. Nowadays, spatiotemporal graph data is becoming increasingly popular and important, ranging from microscale (e. g. protein folding), to middle-scale (e. g. dynamic functional connectivity), to macro-scale (e. g. human mobility network). Although disentangling and understanding the correlations among spatial, temporal, and graph aspects have been a long-standing key topic in network science, they typically rely on network processing hypothesized by human knowledge. This usually fit well towards the graph properties which can be predefined, but cannot do well for the most cases, especially for many key domains where the human has yet very limited knowledge such as protein folding and biological neuronal networks. In this paper, we aim at pushing forward the modeling and understanding of spatiotemporal graphs via new disentangled deep generative models. Specifically, a new Bayesian model is proposed that factorizes spatiotemporal graphs into spatial, temporal, and graph factors as well as the factors that explain the interplay among them. A variational objective function and new mutual information thresholding algorithms driven by information bottleneck theory have been proposed to maximize the disentanglement among the factors with theoretical guarantees. Qualitative and quantitative experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed model over the state-of-the-arts by up to 69. 2% for graph generation and 41. 5% for interpretability.

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IJCAI Conference 2022 Conference Paper

Few-Shot Learning on Graphs

  • Chuxu Zhang
  • Kaize Ding
  • Jundong Li
  • Xiangliang Zhang
  • Yanfang Ye
  • Nitesh V. Chawla
  • Huan Liu

Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming. In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge. There have been many studies working on FSLG recently. In this paper, we comprehensively survey these work in the form of a series of methods and applications. Specifically, we first introduce FSLG challenges and bases, then categorize and summarize existing work of FSLG in terms of three major graph mining tasks at different granularity levels, i. e. , node, edge, and graph. Finally, we share our thoughts on some future research directions of FSLG. The authors of this survey have contributed significantly to the AI literature on FSLG over the last few years.

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NeurIPS Conference 2022 Conference Paper

Multi-objective Deep Data Generation with Correlated Property Control

  • Shiyu Wang
  • Xiaojie Guo
  • Xuanyang Lin
  • Bo Pan
  • Yuanqi Du
  • Yinkai Wang
  • Yanfang Ye
  • Ashley Petersen

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design. However, the advance of deep generative models is limited by the challenges to generate objects that possess multiple desired properties because: 1) the existence of complex correlation among real-world properties is common but hard to identify; 2) controlling individual property enforces an implicit partially control of its correlated properties, which is difficult to model; 3) controlling multiple properties under variour manners simultaneously is hard and underexplored. We address these challenges by proposing a novel deep generative framework that recovers semantics and correlation of properties through disentangled latent vectors. The correlation is handled via an explainable mask pooling layer, and properties are precisely retained by the generated objects via the mutual dependence between latent vectors and properties. Our generative model preserves properties of interest while handles correlation and conflicts of properties under a multi-objective optimization framework. The experiments demonstrate our model's superior performance in generating objects with desired properties.

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IJCAI Conference 2022 Conference Paper

Rx-refill Graph Neural Network to Reduce Drug Overprescribing Risks (Extended Abstract)

  • Jianfei Zhang
  • Ai-Te Kuo
  • Jianan Zhao
  • Qianlong Wen
  • Erin Winstanley
  • Chuxu Zhang
  • Yanfang Ye

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. In this paper, we propose a novel model RxNet, which builds 1) a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various patients, 2) an RxLSTM network to explore the dynamic Rx-refill behavior and medical condition variation of patients, and 3) a dosing-adaptive network to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a one-year state-wide PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse.

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IJCAI Conference 2021 Conference Paper

Adapting Meta Knowledge with Heterogeneous Information Network for COVID-19 Themed Malicious Repository Detection

  • Yiyue Qian
  • Yiming Zhang
  • Yanfang Ye
  • Chuxu Zhang

As cyberattacks caused by malware have proliferated during the pandemic, building an automatic system to detect COVID-19 themed malware in social coding platforms is in urgent need. The existing methods mainly rely on file content analysis while ignoring structured information among entities in social coding platforms. Additionally, they usually require sufficient data for model training, impairing their performances over cases with limited data which is common in reality. To address these challenges, we develop Meta-AHIN, a novel model for COVID-19 themed malicious repository detection in GitHub. In Meta-AHIN, we first construct an attributed heterogeneous information network (AHIN) to model the code content and social coding properties in GitHub; and then we exploit attention-based graph convolutional neural network (AGCN) to learn repository embeddings and present a meta-learning framework for model optimization. To utilize unlabeled information in AHIN and to consider task influence of different types of repositories, we further incorporate node attribute-based self-supervised module and task-aware attention weight into AGCN and meta-learning respectively. Extensive experiments on the collected data from GitHub demonstrate that Meta-AHIN outperforms state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

Disentangled Representation Learning in Heterogeneous Information Network for Large-scale Android Malware Detection in the COVID-19 Era and Beyond

  • Shifu Hou
  • Yujie Fan
  • Mingxuan Ju
  • Yanfang Ye
  • Wenqiang Wan
  • Kui Wang
  • Yinming Mei
  • Qi Xiong

In the fight against the COVID-19 pandemic, many social activities have moved online; society’s overwhelming reliance on the complex cyberspace makes its security more important than ever. In this paper, we propose and develop an intelligent system named Dr. HIN to protect users against the evolving Android malware attacks in the COVID-19 era and beyond. In Dr. HIN, besides app content, we propose to consider higherlevel semantics and social relations among apps, developers and mobile devices to comprehensively depict Android apps; and then we introduce a structured heterogeneous information network (HIN) to model the complex relations and exploit meta-path guided strategy to learn node (i. e. , app) representations from HIN. As the representations of malware could be highly entangled with benign apps in the complex ecosystem of development, it poses a new challenge of learning the latent explanatory factors hidden in the HIN embeddings to detect the evolving malware. To address this challenge, we propose to integrate domain priors generated from different views (i. e. , app content, app authorship, app installation) to devise an adversarial disentangler to separate the distinct, informative factors of variations hidden in the HIN embeddings for large-scale Android malware detection. This is the first attempt of disentangled representation learning in HIN data. Promising experimental results based on real sample collections from security industry demonstrate the performance of Dr. HIN in evolving Android malware detection, by comparison with baselines and popular mobile security products.

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AAAI Conference 2021 Conference Paper

Heterogeneous Graph Structure Learning for Graph Neural Networks

  • Jianan Zhao
  • Xiao Wang
  • Chuan Shi
  • Binbin Hu
  • Guojie Song
  • Yanfang Ye

Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention in recent years and achieved outstanding performance in many tasks. The success of the existing HGNNs relies on one fundamental assumption, i. e. , the original heterogeneous graph structure is reliable. However, this assumption is usually unrealistic, since the heterogeneous graph in reality is inevitably noisy or incomplete. Therefore, it is vital to learn the heterogeneous graph structure for HGNNs rather than rely only on the raw graph structure. In light of this, we make the first attempt towards learning an optimal heterogeneous graph structure for HGNNs and propose a novel framework HGSL, which jointly performs Heterogeneous Graph Structure Learning and GNN parameter learning for classification. Different from traditional homogeneous graph structure learning, considering the heterogeneity of different relations in heterogeneous graph, HGSL generates each relation subgraph separately. Specifically, in each generated relation subgraph, HGSL not only considers the feature similarity by generating feature similarity graph, but also considers the complex heterogeneous interactions in features and semantics by generating feature propagation graph and semantic graph. Then, these graphs are fused to a learned heterogeneous graph and optimized together with a GNN towards classification objective. Extensive experiments on real-world graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods.

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TIST Journal 2021 Journal Article

Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion

  • Chuanbo Hu
  • Minglei Yin
  • Bin Liu
  • Xin Li
  • Yanfang Ye

Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge for the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG). Nearly 4,000 user accounts, of which more than 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.

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AAAI Conference 2021 Conference Paper

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

  • Chao Huang
  • Huance Xu
  • Yong Xu
  • Peng Dai
  • Lianghao Xia
  • Mengyin Lu
  • Liefeng Bo
  • Hao Xing

Social recommendation task aims to predict users’ preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users’ social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques. To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the highorder user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. Additionally, we further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns. Experimental studies on real-world datasets show the effectiveness of our method against many strong baselines in a variety of settings. Source codes are available at: https: //github. com/xhcdream/KCGN.

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JBHI Journal 2020 Journal Article

$\alpha$-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States

  • Yanfang Ye
  • Shifu Hou
  • Yujie Fan
  • Yiming Zhang
  • Yiyue Qian
  • Shiyu Sun
  • Qian Peng
  • Mingxuan Ju

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e. g. , disease related data, demographic, mobility and social media data), in this work, we propose and develop an AI-driven system (named α-Satellite), as an initial offering, to provide dynamic COVID-19 risk assessment in the United States. More specifically, given a point of interest (POI), the system will automatically provide risk indices associated with it in a hierarchical manner (e. g. , state, county, POI) to enable people to select appropriate actions for protection while minimizing disruptions to daily life. To comprehensively evaluate our system for dynamic COVID-19 risk assessment, we first conduct a set of empirical studies; and then we validate it based on a real-world dataset consisting of 5, 060 annotated POIs, which achieves the area of under curve (AUC) of 0. 9202. As of June 18, 2020, α-Satellite has had 56, 980 users. Based on the feedback from its large-scale users, we perform further analysis and have three key findings: i) people from more severe regions (i. e. , with larger numbers of COVID-19 cases) have stronger interests using our system to assist with actionable information; ii) users are more concerned about their nearby areas in terms of COVID-19 risks; iii) the user feedback about their perceptions towards COVID-19 risks of their query POIs indicate the challenge of public concerns about the safety versus its negative effects on society and the economy. Our system and generated datasets have been made publicly accessible via our website.

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IJCAI Conference 2020 Conference Paper

Network Schema Preserving Heterogeneous Information Network Embedding

  • Jianan Zhao
  • Xiao Wang
  • Chuan Shi
  • Zekuan Liu
  • Yanfang Ye

As heterogeneous networks have become increasingly ubiquitous, Heterogeneous Information Network (HIN) embedding, aiming to project nodes into a low-dimensional space while preserving the heterogeneous structure, has drawn increasing attention in recent years. Many of the existing HIN embedding methods adopt meta-path guided random walk to retain both the semantics and structural correlations between different types of nodes. However, the selection of meta-paths is still an open problem, which either depends on domain knowledge or is learned from label information. As a uniform blueprint of HIN, the network schema comprehensively embraces the high-order structure and contains rich semantics. In this paper, we make the first attempt to study network schema preserving HIN embedding, and propose a novel model named NSHE. In NSHE, a network schema sampling method is first proposed to generate sub-graphs (i. e. , schema instances), and then multi-task learning task is built to preserve the heterogeneous structure of each schema instance. Besides preserving pairwise structure information, NSHE is able to retain high-order structure (i. e. , network schema). Extensive experiments on three real-world datasets demonstrate that our proposed model NSHE significantly outperforms the state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow

  • Yujie Fan
  • Yiming Zhang
  • Shifu Hou
  • Lingwei Chen
  • Yanfang Ye
  • Chuan Shi
  • Liang Zhao
  • Shouhuai Xu

As modern social coding platforms such as GitHub and Stack Overflow become increasingly popular, their potential security risks increase as well (e. g. , risky or malicious codes could be easily embedded and distributed). To enhance the social coding security, in this paper, we propose to automate cross-platform user identification between GitHub and Stack Overflow to combat the attackers who attempt to poison the modern software programming ecosystem. To solve this problem, an important insight brought by this work is to leverage social coding properties in addition to user attributes for cross-platform user identification. To depict users in GitHub and Stack Overflow (attached with attributed information), projects, questions and answers as well as the rich semantic relations among them, we first introduce an attributed heterogeneous information network (AHIN) for modeling. Then, we propose a novel AHIN representation learning model AHIN2Vec to efficiently learn node (i. e. , user) representations in AHIN for cross-platform user identification. Comprehensive experiments on the data collections from GitHub and Stack Overflow are conducted to validate the effectiveness of our developed system iDev integrating our proposed method in cross-platform user identification by comparisons with other baselines.

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AAAI Conference 2019 Conference Paper

Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting

  • Yuyang Gao
  • Liang Zhao
  • Lingfei Wu
  • Yanfang Ye
  • Hui Xiong
  • Chaowei Yang

Due to the potentially significant benefits for society, forecasting spatio-temporal societal events is currently attracting considerable attention from researchers. Beyond merely predicting the occurrence of future events, practitioners are now looking for information about specific subtypes of future events in order to allocate appropriate amounts and types of resources to manage such events and any associated social risks. However, forecasting event subtypes is far more complex than merely extending binary prediction to cover multiple classes, as 1) different locations require different models to handle their characteristic event subtype patterns due to spatial heterogeneity; 2) historically, many locations have only experienced a incomplete set of event subtypes, thus limiting the local model’s ability to predict previously “unseen” subtypes; and 3) the subtle discrepancy among different event subtypes requires more discriminative and profound representations of societal events. In order to address all these challenges concurrently, we propose a Spatial Incomplete Multi-task Deep leArning (SIMDA) framework that is capable of effectively forecasting the subtypes of future events. The new framework formulates spatial locations into tasks to handle spatial heterogeneity in event subtypes, and learns a joint deep representation of subtypes across tasks. Furthermore, based on the “first law of geography”, spatiallyclosed tasks share similar event subtype patterns such that adjacent tasks can share knowledge with each other effectively. Optimizing the proposed model amounts to a new nonconvex and strongly-coupled problem, we propose a new algorithm based on Alternating Direction Method of Multipliers (ADMM) that can decompose the complex problem into subproblems that can be solved efficiently. Extensive experiments on six real-world datasets demonstrate the effectiveness and efficiency of the proposed model.

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IJCAI Conference 2019 Conference Paper

Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection

  • Yanfang Ye
  • Shifu Hou
  • Lingwei Chen
  • Jingwei Lei
  • Wenqiang Wan
  • Jiabin Wang
  • Qi Xiong
  • Fudong Shao

The increasingly sophisticated Android malware calls for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. To model different types of entities (i. e. , app, API, device, signature, affiliation) and rich relations among them, we present a structured heterogeneous graph (HG) for modeling. To efficiently classify nodes (e. g. , apps) in the constructed HG, we propose the HG-Learning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without rerunning/adjusting HG embeddings at the first attempt. We later design a deep neural network classifier taking the learned HG representations as inputs for real-time Android malware detection. Comprehensive experiments on large-scale and real sample collections from Tencent Security Lab are performed to compare various baselines. Promising results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.

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IJCAI Conference 2018 Conference Paper

Automatic Opioid User Detection from Twitter: Transductive Ensemble Built on Different Meta-graph Based Similarities over Heterogeneous Information Network

  • Yujie Fan
  • Yiming Zhang
  • Yanfang Ye
  • Xin Li

Opioid (e. g. , heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, in this paper, we propose a novel framework named HinOPU to automatically detect opioid users from Twitter, which will assist in sharpening our understanding toward the behavioral process of opioid addiction and treatment. In HinOPU, to model the users and the posted tweets as well as their rich relationships, we introduce structured heterogeneous information network (HIN) for representation. Afterwards, we use meta-graph based approach to characterize the semantic relatedness over users; we then formulate different similarities over users based on different meta-graphs on HIN. To reduce the cost of acquiring labeled samples for supervised learning, we propose a transductive classification method to build the base classifiers based on different similarities formulated by different meta-graphs. Then, to further improve the detection accuracy, we construct an ensemble to combine different predictions from different base classifiers for opioid user detection. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of HinOPU in opioid user detection by comparisons with other alternate methods.

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IJCAI Conference 2018 Conference Paper

Make Evasion Harder: An Intelligent Android Malware Detection System

  • Shifu Hou
  • Yanfang Ye
  • Yangqiu Song
  • Melih Abdulhayoglu

To combat the evolving Android malware attacks, in this paper, instead of only using Application Programming Interface (API) calls, we further analyze the different relationships between them and create higher-level semantics which require more efforts for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning to make predictions. Promising experimental results based on real sample collections from Comodo Cloud Security Center demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.

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TIST Journal 2015 Journal Article

Soter

  • Yanfang Ye
  • Tao Li
  • Haiyin Shen

In recent years, crimes against children and cases of missing children have increased at a high rate. Therefore, there is an urgent need for safety support systems to prevent crimes against children or for antiloss, especially when parents are not with their children, such as to and from school. However, existing children’s tracking systems are not smart enough to provide the safety supports, as they simply locate the children’s positions without offering any notification to parents that their children may be in danger. In addition, there is limited research on children’s tracking and their antiloss. In this article, based on location histories, we introduce novel notions of children’s life patterns that capture their general lifestyles and regularities, and develop an intelligent data mining framework to learn the safe regions and safe routes of children on the cloud side. When the children may be in danger, their parents will receive automatic notifications from the cloud. We also propose an effective energy-efficient positioning scheme that leverages the location tracking accuracy of the children while keeping energy overhead low by using a hybrid global positioning system and a global system for mobile communications. To the best of our knowledge, this is the first attempt in applying data mining techniques to applications designed for children’s safety. Our proposed techniques have been incorporated into Soter, a children’s safeguard system that is used to provide cloud service for smart bracelets produced by Qihoo. The case studies on real smart bracelet users of Qihoo demonstrate the effectiveness of our proposed methods and Soter for children’s safety.

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v2026.09.13