1 citations · 3 across the 13 of their papers we have counts for
14 papers
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Quanxin Wang, Xuanting Xie, Bingheng Li +4
Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a…
Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence
Haolu Liu, Xiyue Wang, Xuanting Xie +2
Incomplete multi-view clustering (IMVC) is typically evaluated by retraining separate models under different missing-view configurations. Evaluations indexed only by nominal missin…
Clustering as Reasoning: A -Means Interpretation of Chain-of-Thought Graph Learning
Xuanting Xie, Zhaochen Guo, Bingheng Li +4
Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-…
GMoPE:A Prompt-Expert Mixture Framework for Graph Foundation Models
Zhibin Wang, Zhixing Zhang, Shuqi Wang +2
Graph Neural Networks (GNNs) have demonstrated impressive performance on task-specific benchmarks, yet their ability to generalize across diverse domains and tasks remains limited.…
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…
On the Benefits of Attribute-Driven Graph Domain Adaptation
Ruiyi Fang, Bingheng Li, Zhao Kang +5
Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Rece…
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…
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…
Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering
Xiaowei Qian, Bingheng Li, Zhao Kang
Multi-relational clustering is a challenging task due to the fact that diverse semantic information conveyed in multi-layer graphs is difficult to extract and fuse. Recent methods…