Non-Local Graph Neural Networks

Article Computer Science, Artificial Intelligence

Deep Learning on Graphs: A Survey

Ziwei Zhang et al.

Summary: This survey comprehensively reviews the application of deep learning methods on graph data. The existing methods are categorized into five types, and their development history, differences, and compositions are covered in a systematic manner. Potential future research directions are also discussed.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

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Article Computer Science, Artificial Intelligence

A Comprehensive Survey on Graph Neural Networks

Zonghan Wu et al.

Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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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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Article Mathematics, Interdisciplinary Applications

Multi-Scale attributed node embedding

Benedek Rozemberczki et al.

Summary: Our network embedding algorithms capture information from the local distribution over node attributes, either by pooling observations from different-sized neighborhoods or encoding them distinctly in a multi-scale approach. The algorithms are computationally efficient and outperform comparable models on social networks and web graphs. This approach is useful for a range of applications, including identifying latent features across disconnected networks with similar attributes.

JOURNAL OF COMPLEX NETWORKS (2021)

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Proceedings Paper Computer Science, Information Systems