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Xinli Huang

2 accepted papers

2025

MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering

ACL 2025finding

This paper studies the problem of text-attributed graph clustering, which aims to cluster each node into different groups using both textual attributes and structural information. Although graph neural networks (GNNs) have been proposed to solve this problem, their performance is usually limited whe…

Cited by 0SourcePDFScholar
2024

Federated Learning on Distributed Graphs Considering Multiple Heterogeneities

ICASSP 2024accepted

Federated graph learning (FGL) collaboratively learns a global graph neural network with distributed graphs, where a significant challenge is addressing non-IID issues. Existing work has not fully explored and utilized the intrinsic features of graphs, resulting in their inability to effectively sol…

Cited by 0SourceScholar