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cs.LG2026
Towards One-for-All Foundation Model for Attributed Graph Clustering
Yunhui Liu, Xudong Jin, Kang Zhang +5
Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph topology, yet its unsupervised nature makes model selection and adaptation…
cs.LG2026
Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering
Yunhui Liu, Pengyu Qiu, Yu Xing +6
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. De…
cs.LG2024
Graph Fairness Learning under Distribution Shifts
Yibo Li, Xiao Wang, Yujie Xing +4
Graph neural networks (GNNs) have achieved remarkable performance on graph-structured data. However, GNNs may inherit prejudice from the training data and make discriminatory predi…