A novel message passing neural network based on neighborhood expansion

Article Computer Science, Artificial Intelligence

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

Jianxin Li et al.

Summary: Graph neural networks (GNNs) are widely used in deep learning for graph analysis tasks. However, current methods ignore heterogeneity in real-world graphs and fail to capture content-based correlations between nodes. In this paper, we propose a novel HAE framework and a HAE(GNN) model that incorporates meta-paths and meta-graphs for rich, heterogeneous semantics and leverages self-attention mechanism for exploring content-based interactions between nodes.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

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Article Computer Science, Artificial Intelligence

Non-Local Graph Neural Networks

Meng Liu et al.

Summary: In this work, a simple yet effective non-local aggregation framework with efficient attention-guided sorting is proposed for tasks on disassortative graphs. Thorough experiments show that the proposed method significantly outperforms previous state-of-the-art methods in terms of both model performance and efficiency on various benchmark datasets.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

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Article Computer Science, Information Systems

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

Hao Peng et al.

Summary: In this article, the authors propose a novel multi-relational Graph Neural Network architecture called RIOGNN, which addresses the complexity and diversity issues in heterogeneous graphs. By using reinforced, recursive, and flexible neighborhood selection, RIOGNN achieves more discriminative node embeddings with enhanced explainability. It also demonstrates advancements in effectiveness and efficiency compared to other comparative GNN models.

ACM TRANSACTIONS ON INFORMATION SYSTEMS (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

Node Similarity Preserving Graph Convolutional Networks

Wei Jin et al.

Summary: The SimP-GCN framework balances information from graph structure and node features by preserving feature similarity during aggregation process, effectively maintaining node similarity.

WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Interpreting and Unifying Graph Neural Networks with An Optimization Framework

Meiqi Zhu et al.

Summary: This paper studies the propagation mechanisms of graph neural networks, proposes a unified optimization framework, summarizes the commonalities between various GNNs, and introduces novel optimization strategies to flexibly design new graph neural networks.

PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021) (2021)

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Review Computer Science, Artificial Intelligence