Papers › Attributed Graph Clustering via Adaptive Graph Convolution

Attributed Graph Clustering via Adaptive Graph Convolution

4 Jun 2019arXiv:1906.01210archive 2025-07-28

Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu

Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promising clustering performance on some real attributed networks. However, there is limited understanding of how graph convolution affects clustering performance and how to properly use it to optimize performance for different graphs. Existing methods essentially use graph convolution of a fixed and low order that only takes into account neighbours within a few hops of each node, which underutilizes node relations and ignores the diversity of graphs. In this paper, we propose an adaptive graph convolution method for attributed graph clustering that exploits high-order graph convolution to capture global cluster structure and adaptively selects the appropriate order for different graphs. We establish the validity of our method by theoretical analysis and extensive experiments on benchmark datasets. Empirical results show that our method compares favourably with state-of-the-art methods.

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Code

karenlatong/AGC-master officialtf report

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Tasks

Clustering Community Detection Diversity Graph Clustering

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Graph Clustering	Citeseer	AGC	ACC	67.0	#3 of 9	Archive leaderboard	report
Graph Clustering	Citeseer	AGC	NMI	41.13	#3 of 9	Archive leaderboard	report
Graph Clustering	Cora	AGC	ACC	68.92	#3 of 9	Archive leaderboard	report
Graph Clustering	Cora	AGC	NMI	53.68	#3 of 9	Archive leaderboard	report
Graph Clustering	Pubmed	AGC	ACC	69.78	#5 of 7	Archive leaderboard	report
Graph Clustering	Pubmed	AGC	NMI	31.59	#5 of 7	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution Graph Convolutional Networks

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