3 papers
cs.LG2025
Oldie but Goodie: Re-illuminating Label Propagation on Graphs with Partially Observed Features
Sukwon Yun, Xin Liu, Yunhak Oh +4
In real-world graphs, we often encounter missing feature situations where a few or the majority of node features, e.g., sensitive information, are missed. In such scenarios, direct…
cs.IR2024
How Powerful is Graph Filtering for Recommendation
Shaowen Peng, Xin Liu, Kazunari Sugiyama +1
It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a…
cs.IR2024
Balancing Embedding Spectrum for Recommendation
Shaowen Peng, Kazunari Sugiyama, Xin Liu +1
Modern recommender systems heavily rely on high-quality representations learned from high-dimensional sparse data. While significant efforts have been invested in designing powerfu…