TY - JOUR
AU - Wang, Nancy
AB - 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.
TI - Improving graph collaborative filtering with network motifs
JO - Neural Computing and Applications
DO - 10.1007/s00521-025-11079-8
DA - 2025-06-01
UR - https://www.deepdyve.com/lp/springer-journals/improving-graph-collaborative-filtering-with-network-motifs-kirhDSshJv
SP - 9413
EP - 9432
VL - 37
IS - 16
DP - DeepDyve
ER -