5 papers
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…
GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks
Jiarui Tan, Zhongjian Zhang, YaBo Guo +5
Large language model (LLM) agents are increasingly capable of planning, using tools, and interacting with external environments. They are typically supported by harnesses, which ma…
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…
Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework
Yujie Xing, Xiao Wang, Bin Wu +2
Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning due to their ability to model diverse node interactions. However, existing GTs often…
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…