1 citations · 2 across the 12 of their papers we have counts for
5 papers · 1 filter
One Node One Model: Featuring the Missing-Half for Graph Clustering
Xuanting Xie, Bingheng Li, Erlin Pan +3
Most existing graph clustering methods primarily focus on exploiting topological structure, often neglecting the ``missing-half" node feature information, especially how these feat…
Simplified PCNet with Robustness
Bingheng Li, Xuanting Xie, Haoxiang Lei +2
Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs. However, they cannot general…
Robust Graph Structure Learning under Heterophily
Xuanting Xie, Zhao Kang, Wenyu Chen
Graph is a fundamental mathematical structure in characterizing relations between different objects and has been widely used on various learning tasks. Most methods implicitly assu…
CDC: A Simple Framework for Complex Data Clustering
Zhao Kang, Xuanting Xie, Bingheng Li +1
In today's data-driven digital era, the amount as well as complexity, such as multi-view, non-Euclidean, and multi-relational, of the collected data are growing exponentially or ev…
Provable Filter for Real-world Graph Clustering
Xuanting Xie, Erlin Pan, Zhao Kang +2
Graph clustering, an important unsupervised problem, has been shown to be more resistant to advances in Graph Neural Networks (GNNs). In addition, almost all clustering methods foc…