Author name cluster

Xiaobin Hong

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.

8 papers

1 author row

AAAI Conference 2026 Conference Paper

Learnable Matrix Profile for Motif Discovery on Multivariate Time Series

Mingkai Lin
Yinke Wang
Xiaobin Hong
Wenzhong Li

Multivariate motif discovery aims to identify frequently occurring subsequences within multi-dimensional time series, which is a critical machine learning task with wide applications. However, previous motif discovery algorithms often miss complex multivariate motifs and struggle with high computational costs as data scale and dimensionality grow. We propose a novel learnable multivariate matrix profile method (L-MAP) that captures inter-dimensional dependencies for comprehensive analysis of multivariate time series. The time series is partitioned into subsequences using the Fourier transform in the frequency domain, with locality-sensitive hashing (LSH) assigning them to buckets based on distinct patterns. Each subsequence is modeled as a graph for multivariate fusion, where triplet learning is used to capture cross-dimensional relationships and form graph embeddings. Unlike prior methods relying on Euclidean distance modeling, our graph-based approach computes all-pairs similarity in a latent space, which constructs the multivariate matrix profile from distributions formed by embedding clusters. Extensive experiments on multivariate datasets from diverse domains demonstrate that L-MAP outperforms state-of-the-art methods in motif discovery, offering superior quality, diversity, and scalability efficiency.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Multimodal Graph Representation Learning with Dynamic Information Pathways

Xiaobin Hong
Mingkai Lin
Xiaoli Wang
Chaoqun Wang
Wenzhong Li

Multimodal graphs, where nodes contain heterogeneous features such as images and text, are increasingly common in real-world applications. Effectively learning on such graphs requires both adaptive intra-modal message passing and efficient inter-modal aggregation. However, most existing approaches to multimodal graph learning are typically extended from conventional graph neural networks and rely on static structures or dense attention, which limit flexibility and expressive node embedding learning. In this paper, we propose a novel multimodal graph representation learning framework with Dynamic information Pathways (DiP). By introducing modality-specific pseudo nodes, DiP enables dynamic message routing within each modality via proximity-guided pseudo-node interactions and captures inter-modality dependence through efficient information pathways in a shared state space. This design achieves adaptive, expressive, and sparse message propagation across modalities with linear complexity. We conduct the link prediction and node classification tasks to evaluate performance and carry out full experimental analyses. Extensive experiments across multiple benchmarks demonstrate that DiP consistently outperforms baselines.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Aggregation Mechanism Based Graph Heterogeneous Networks Distillation

Xiaobin Hong
Mingkai Lin
Xiangkai Ma
Wenzhong Li
Sanglu Lu

Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness across various tasks but are often hindered by their high computational overhead. GNN-to-MLP distillation provides a promising remedy by transferring knowledge from complex GNNs to lightweight MLPs. However, existing methods largely overlook the differences in aggregation mechanisms and heterogeneous architectures. Simplifying such intricate information into MLP potentially causes information loss or distortion, ultimately resulting in suboptimal performance. This paper proposes an aggregation mechanism enhanced GNN distillation framework (AMEND). AMEND introduces multi-scope aggregation context preservation to replicate the teacher's broad aggregation scopes and an aggregation-enhanced centered kernel alignment method to match the teacher's aggregation patterns. To ensure efficient and robust knowledge transfer, we integrate a manifold mixup strategy, enabling the student to capture the teacher's insights into mixed data distributions. Experimental results on 8 standard and 4 large-scale datasets demonstrate that AMEND consistently outperforms state-of-the-art distillation methods.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Contextual Structure Knowledge Transfer for Graph Neural Networks

Zhiyuan Yu
Wenzhong Li
Zhangyue Yin
Xiaobin Hong
Shijian Xiao
Sanglu Lu

Graph transfer learning endeavors to develop a Graph Neural Network (GNN) model in a fully-labeled source domain, with the intention of deploying it on a target domain that has limited labeled data for inference. We reveal that prevalent graph transfer learning methods are susceptible to the homophily shift problem. This issue arises from the divergence in homophily structures between the source and target graphs, leading to a notable deterioration in the performance of GNN models. In this paper, we introduce a novel Contextual Structural Graph Neural Network (CS-GNN) method, leveraging a tailored attention mechanism to apprehend a variety of local structural cues, facilitating structural knowledge transfer across domains. It features an ego-network module to distill local structural diversity and a moment-based approach to gauge structural patterns without needing ground-truth labels. CS-GNN crafts a feature smoothness matrix from node attributes, guiding a customized attention mechanism for feature aggregation. A group-wise fairness loss is employed to balance learning across various structural patterns, enhancing the model's ability to transfer knowledge across domains. Comprehensive experiments conducted on six benchmark datasets substantiate the superiority of CS-GNN over the state-of-the-art methods, demonstrating significant improvements in accuracy and robustness against homophily shifts.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Global-Semantic Alignment Distillation for Partial Multi-view Classification

Xiaoli Wang
Anqi Huang
Yongli Wang
Guanzhou Ke
Xiaobin Hong
Jun Liu

Partial multi-view classification (PMvC) poses a significant challenge due to the incomplete nature of multi-view data, which complicates effective information fusion and accurate classification. Existing PMvC methods typically rely on heuristic evaluations of view informativeness to achieve global alignment for downstream classification tasks. However, these approaches suffer from two critical issues: information redundancy and semantic misalignment. The complexity of missing data not only leads to over-reliance on redundant or less informative views but also exacerbates semantic misalignment across views, making it difficult for existing methods to effectively capture and discriminate the class-related features. To address these issues, this work proposes a novel GLobal-semantic Alignment Distillation (GLAD) model for partial multi-view classification without requiring imputation. Our approach incorporates a self-distillation mechanism that enables the model to extract informative features and achieve global semantic alignment across views. The key insight of GLAD is leveraging labels as semantic anchors to guide the alignment of partial multi-view features. By integrating labels with extracted features via a cross-attention mechanism, we generate ideal embeddings that consistently capture global semantics across views. These embeddings then serve as intermediate supervision for distilling the student model, ensuring robust semantic alignment even with missing views. We further introduce a margin-aware weighting strategy to enhance the model's discriminative ability. Extensive experimental results validate the effectiveness and superiority of the proposed method, showcasing significant improvements in classification performance over existing techniques.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Unified Graph Neural Networks Pre-training for Multi-domain Graphs

Mingkai Lin
Xiaobin Hong
Wenzhong Li
Sanglu Lu

Graph Neural Networks (GNNs) have proven effective and typically benefit from pre-training on accessible graphs to enhance performance on tasks with limited labeled data. However, existing GNNs are constrained by the ``one-domain-one-model'' limitation, which restricts their effectiveness across diverse graph domains. In this paper, we tackle this problem by developing a method called Multi-Domain Pre-training for a Unified GNN Model (MDP-GNN). This method is based on the philosophical notion that everything is interconnected, suggesting that a latent meta-domain exists to encompass the diverse graph domains and their interconnections. MDP-GNN seeks to identify and utilize this meta-domain to train a unified GNN model through three core strategies. Firstly, it integrates node feature semantics from different domains to create unified representations. Secondly, it employs a bi-level learning strategy to build a domain-synthesized network that identifies latent connections to facilitate cross-domain knowledge transfer. Thirdly, it uses Wasserstein distance to map diverse domains into the common meta-domain for graph distribution alignment. We validate the effectiveness of MDP-GNN through theoretical analysis and extensive experiments on four real-world graph datasets, showing its superiority in enhancing GNN performance across diverse domains.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Label Attentive Distillation for GNN-Based Graph Classification

Xiaobin Hong
Wenzhong Li
Chaoqun Wang
Mingkai Lin
Sanglu Lu

Graph Neural Networks (GNNs) have emerged as a powerful tool for modeling graph-structured data, exhibiting remarkable potential in applications such as social networks, recommendation systems, and molecular structures. However, the conventional GNNs perform node-level feature aggregation from neighbors without considering graph-label information, which leads to the misaligned embedding problem that may cause a detrimental effect on graph-level tasks such as graph classification. In this paper, we propose a novel label-attentive distillation method called LAD-GNN for graph representation learning to solve this problem. It alternatively trains a teacher model and a student GNN with a distillation-based approach. In the teacher model, a label-attentive encoder is proposed to encode the label information fusing with the node features to generate ideal embedding. In the student model, the ideal embedding is used as intermediate supervision to urge the student GNN to learn class-friendly node embedding to facilitate graph-level tasks. Generally, LAD-GNN is an enhanced GNN training approach that can be incorporated with arbitrary GNN backbone to improve performance without significant increase of computational cost. Extensive experiments with 7 GNN backbones based on 10 benchmark datasets show that LAD-GNN improves the SOTA GNNs in graph classification accuracy. The source codes of LAD-GNN are publicly available on https://github.com/XiaobinHong/LAD-GNN.

PDF Details DOI

AAAI Conference 2021 Conference Paper

Graph Game Embedding

Xiaobin Hong
Tong Zhang
Zhen Cui
Yuge Huang
Pengcheng Shen
Shaoxin Li
Jian Yang

Graph embedding aims to encode nodes/edges into lowdimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph structures. Inspired by the spirit of game learning, node embedding is converted to the selection/searching process of player strategies, where each node corresponds to one player and each edge corresponds to the interaction of two players. Then, a utility function, which theoretically satisfies the Nash Equilibrium, is defined to measure the benefit/loss of players during graph evolution. Furthermore, a collaboration and competition mechanism is introduced to increase the discriminant learning ability. Under this graph game embedding framework, considering different interaction manners of nodes, we propose two specific models, named paired game embedding for paired nodes and group game embedding for group interaction. Comparing with existing graph embedding methods, our algorithm possesses two advantages: (1) the designed utility function ensures the stable graph evolution with theoretical convergence and Nash Equilibrium satisfaction; (2) the introduced collaboration and competition mechanism endows the graph game embedding framework with discriminative feature leaning ability by guiding each node to learn an optimal strategy distinguished from others. We test the proposed method on three public datasets about citation networks, and the experimental results verify the effectiveness of our method.

PDF Details