High-order multi-view clustering for generic data

Pan, Erlin; Kang, Zhao

doi:10.1016/j.inffus.2023.101947

Cited by 23 publications

(2 citation statements)

References 41 publications

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“…GC-VGE [16] uses a GCN-based variational autoencoder to further enhance node category prediction based on embedding. MCGC [17] proposes an embedding framework for multiscale graph representation learning to learn network information on multiple views. SDCN [18] constructs a dual network of DNN and GCN that proposes dual self-supervised modular optimization learning.…”

Section: Related Work 21 Graph Representation Learningmentioning

confidence: 99%

Hypergraph network embedding for community detection

Xiang

You

Wang

et al. 2023

Preprint

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Using attribute graphs for node embedding to detect community structure has become a popular research topic. However, most of the existing algorithms mainly focus on the network structure and node features, which ignore the higher-order relationships between nodes. In addition, only adopting the original graph structure will suffer from sparsity problems, and will also result in sub-optimal node clustering performance. In this paper, we propose a hypergraph network embedding (HGNE) for community detection to solve the above problems. Firstly, we construct potential connections based on the shared feature information of the nodes. By fusing the original topology with feature-based potential connections, both the explicit and implicit relationships are encoded into the node representations, thus alleviating the sparsity problem. Secondly, for integrating the higher-order relationship, we adopt hypergraph convolution to encode the higher-order correlations. To constrain the quality of the node embedding, the spectral hypergraph embedding loss is utilized. Furthermore, we design a dual-contrast mechanism, which draws similar nodes closer by comparing the representations of different views. This mechanism can efficiently prevent multi-node classes from distorting less-node classes. Finally, the dual-contrast mechanism is jointly optimized with self-training clustering to obtain more robust node representations, thus improving the clustering results. Extensive experiments on five datasets indicate the superiority and effectiveness of HGNE.

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Section: Related Work 21 Graph Representation Learningmentioning

confidence: 99%

Hypergraph network embedding for community detection

Xiang

You

Wang

et al. 2023

Preprint

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“…The graph learning module can effectively address the issue of simultaneously leveraging node attributes and structural relationships. MAGC [ 20 ], MCGC [ 21 ], and HMvC [ 22 ] employ graph learning modules to address the computationally complex nature of neural network parameters. However, their computational efficiency still needs improvement for large datasets, and the use of traditional clustering methods during final clustering results in insufficient stability.…”

Section: Introductionmentioning

confidence: 99%

Sampling clustering based on multi-view attribute structural relations

Tang,

Zhao,

et al. 2024

PLoS ONE

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In light of the exponential growth in information volume, the significance of graph data has intensified. Graph clustering plays a pivotal role in graph data processing by jointly modeling the graph structure and node attributes. Notably, the practical significance of multi-view graph clustering is heightened due to the presence of diverse relationships within real-world graph data. Nonetheless, prevailing graph clustering techniques, predominantly grounded in deep learning neural networks, face challenges in effectively handling multi-view graph data. These challenges include the incapability to concurrently explore the relationships between multiple view structures and node attributes, as well as difficulties in processing multi-view graph data with varying features. To tackle these issues, this research proposes a straightforward yet effective multi-view graph clustering approach known as SLMGC. This approach uses graph filtering to filter noise, reduces computational complexity by extracting samples based on node importance, enhances clustering representations through graph contrastive regularization, and achieves the final clustering outcomes using a self-training clustering algorithm. Notably, unlike neural network algorithms, this approach avoids the need for intricate parameter settings. Comprehensive experiments validate the supremacy of the SLMGC approach in multi-view graph clustering endeavors when contrasted with prevailing deep neural network techniques.

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From multi-view data features to clusters: a unified approach

Dornaika

2023

Artif Intell Rev

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High-order multi-view clustering for generic data

Cited by 23 publications

References 41 publications

Hypergraph network embedding for community detection

Hypergraph network embedding for community detection

Sampling clustering based on multi-view attribute structural relations

From multi-view data features to clusters: a unified approach

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