1 citations · 3 across the 6 of their papers we have counts for
7 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…
Diffusion Sampling Correction via Approximately 10 Parameters
Guangyi Wang, Wei Peng, Lijiang Li +3
While powerful for generation, Diffusion Probabilistic Models (DPMs) face slow sampling challenges, for which various distillation-based methods have been proposed. However, they t…
Learning Directed Acyclic Graphs from Partial Orderings
Ali Shojaie, Wenyu Chen
Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistica…
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…
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…