Improving Graph Collaborative Filtering With Network Motifs
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Springer Science and Business Media LLC
Abstract
Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods.Description
Keywords
46 Information and Computing Sciences, 4611 Machine Learning, Bioengineering, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences, Artificial Intelligence & Image Processing, 4602 Artificial intelligence, 4603 Computer vision and multimedia computation, 4611 Machine learning
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Neural Computing and Applications, ISSN: 0941-0643 (Print); 1433-3058 (Online), Springer Science and Business Media LLC, 1-20. doi: 10.1007/s00521-025-11079-8
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