83 citations · 190 across the 20 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 66 cited
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
Fuli Feng, Weiran Huang, Xiangnan He +3
Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are n…
cs.LG2020★ 32 cited
CatGCN: Graph Convolutional Networks with Categorical Node Features
Weijian Chen, Fuli Feng, Qifan Wang +4
Recent studies on Graph Convolutional Networks (GCNs) reveal that the initial node representations (i.e., the node representations before the first-time graph convolution) largely…
cs.IR2020★ 83 cited
Graph Convolution Machine for Context-aware Recommender System
Jiancan Wu, Xiangnan He, Xiang Wang +4
The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, s…