14 citations · 18 across the 8 of their papers we have counts for
9 papers
Attention Beyond Neighborhoods: Reviving Transformer for Graph Clustering
Xuanting Xie, Bingheng Li, Erlin Pan +3
Attention mechanisms have become a cornerstone in modern neural networks, driving breakthroughs across diverse domains. However, their application to graph structured data, where c…
Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing
Xuanting Xie, Bingheng Li, Erlin Pan +2
Graph Neural Networks (GNNs) have become a dominant approach to learning graph representations, primarily because of their message-passing mechanisms. However, GNNs typically adopt…
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…
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…
PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering
Bingheng Li, Erlin Pan, Zhao Kang
Recently, many carefully crafted graph representation learning methods have achieved impressive performance on either strong heterophilic or homophilic graphs, but not both. Theref…
Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
Erlin Pan, Zhao Kang
Graph neural networks (GNNs) based methods have achieved impressive performance on node clustering task. However, they are designed on the homophilic assumption of graph and cluste…
High-order Multi-view Clustering for Generic Data
Erlin Pan, Zhao Kang
Graph-based multi-view clustering has achieved better performance than most non-graph approaches. However, in many real-world scenarios, the graph structure of data is not given or…
Multi-view Contrastive Graph Clustering
Erlin Pan, Zhao Kang
With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either fo…