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On Graph Representation for Attributed Hypergraph Clustering
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Attributed Hypergraph Clustering (AHC) aims at partitioning a hypergraph into clusters such that nodes in the same cluster are close to each other with both high connectedness and homogeneous attributes. Existing AHC methods are all based on matrix factorization which may incur a substantial computation cost; more importantly, they inherently require a prior knowledge of the number of clusters as an input which, if inaccurately estimated, shall lead to a significant deterioration in the clustering quality. In this paper, we propose <u>A</u>ttributed <u>H</u>ypergraph <u>R</u>epresentation for <u>C</u>lustering (AHRC), a cluster-number-free hypergraph clustering consisting of an effective integration of the hypergraph topology and node attributes for hypergraph representation, a multi-hop modularity function for optimization, and a hypergraph sparsification for scalable computation. AHRC achieves cutting-edge clustering quality and efficiency: compared to the state-of-the-art (SOTA) AHC method on 10 real hypergraphs, AHRC obtains an average of 20% higher F-measure, 24% higher ARI, 26% higher Jaccard Similarity, 10% higher Purity, and runs 5.5× faster. As a byproduct, the intermediate result of graph representation dramatically boosts the clustering quality of SOTA contrastive-learning-based hypergraph clustering methods, showing the generality of our graph representation.
Association for Computing Machinery (ACM)
Title: On Graph Representation for Attributed Hypergraph Clustering
Description:
Attributed Hypergraph Clustering (AHC) aims at partitioning a hypergraph into clusters such that nodes in the same cluster are close to each other with both high connectedness and homogeneous attributes.
Existing AHC methods are all based on matrix factorization which may incur a substantial computation cost; more importantly, they inherently require a prior knowledge of the number of clusters as an input which, if inaccurately estimated, shall lead to a significant deterioration in the clustering quality.
In this paper, we propose <u>A</u>ttributed <u>H</u>ypergraph <u>R</u>epresentation for <u>C</u>lustering (AHRC), a cluster-number-free hypergraph clustering consisting of an effective integration of the hypergraph topology and node attributes for hypergraph representation, a multi-hop modularity function for optimization, and a hypergraph sparsification for scalable computation.
AHRC achieves cutting-edge clustering quality and efficiency: compared to the state-of-the-art (SOTA) AHC method on 10 real hypergraphs, AHRC obtains an average of 20% higher F-measure, 24% higher ARI, 26% higher Jaccard Similarity, 10% higher Purity, and runs 5.
5× faster.
As a byproduct, the intermediate result of graph representation dramatically boosts the clustering quality of SOTA contrastive-learning-based hypergraph clustering methods, showing the generality of our graph representation.
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