Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
On Graph Representation for Attributed Hypergraph Clustering
View through CrossRef
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.
Related Results
Completion and decomposition of hypergraphs by domination hypergraphs
Completion and decomposition of hypergraphs by domination hypergraphs
A graph consists of a finite non-empty set of vertices and a set of unordered pairs of vertices, called edges. A dominating set of a graph is a set of vertices D such that every ve...
Graph convolutional neural networks for 3D data analysis
Graph convolutional neural networks for 3D data analysis
(English) Deep Learning allows the extraction of complex features directly from raw input data, eliminating the need for hand-crafted features from the classical Machine Learning p...
T-HyperGNNs: Hypergraph Neural Networks Via Tensor Representations
T-HyperGNNs: Hypergraph Neural Networks Via Tensor Representations
<p>Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial a...
T-HyperGNNs: Hypergraph Neural Networks Via Tensor Representations
T-HyperGNNs: Hypergraph Neural Networks Via Tensor Representations
<p>Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial a...
Adaptive Graph Convolution Using Heat Kernel for Attributed Graph Clustering
Adaptive Graph Convolution Using Heat Kernel for Attributed Graph Clustering
Attributed graphs contain a lot of node features and structural relationships, and how to utilize their inherent information sufficiently to improve graph clustering performance ha...
Bilangan Terhubung Titik Pelangi pada Graf Garis dan Graf Tengah dari Hasil Operasi Comb Graf Bintang C<sub>3</sub> dan Graf Bintang S<sub>n</sub>
Bilangan Terhubung Titik Pelangi pada Graf Garis dan Graf Tengah dari Hasil Operasi Comb Graf Bintang C<sub>3</sub> dan Graf Bintang S<sub>n</sub>
Penelitian ini bertujuan menentukan bilangan terhubung titik pelangi (rainbow vertex connection number) pada graf garis dan graf tengah yang diperoleh dari hasil operasi comb antar...
The Kernel Rough K-Means Algorithm
The Kernel Rough K-Means Algorithm
Background:
Clustering is one of the most important data mining methods. The k-means
(c-means ) and its derivative methods are the hotspot in the field of clustering research in re...
GRACE: A General Graph Convolution Framework for Attributed Graph Clustering
GRACE: A General Graph Convolution Framework for Attributed Graph Clustering
Attributed graph clustering (AGC) is an important problem in graph mining as more and more complex data in real-world have been represented in graphs with attributed nodes. While i...