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. 2022 Aug 17;24(8):1141.
doi: 10.3390/e24081141.

Community Detection in Semantic Networks: A Multi-View Approach

Hailu Yang  1 , Qian Liu  1 , Jin Zhang  2 , Xiaoyu Ding  3 , Chen Chen  1 , Lili Wang  1
Affiliations

Community Detection in Semantic Networks: A Multi-View Approach

Hailu Yang et al. Entropy (Basel). .

Abstract

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.

Keywords: adaptive loss function; community detection; multi-view clustering; semantic information processing; semantic social network.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
The process of multi-view feature representation of social networks.
Figure 2
Figure 2
The data storage matrix for social networks.
Figure 3
Figure 3
Community structure from the view of word frequency of microblog dataset.
Figure 4
Figure 4
Community structure from the view of keywords of the microblog dataset.
Figure 5
Figure 5
The Q value with different number of neighbors and topics. (a) Modularity Q varies with the number of topics (b) Modularity Q varies with the number of neighbors.
Figure 6
Figure 6
Community structure from the view of topic of microblog dataset.
Figure 7
Figure 7
Result of single-view and multi-view community detection of microblog datasets with word frequency = 12,500, keyword = 3000, and topic = 30.
Figure 8
Figure 8
Performance ALMV with different parameter σ. (a) Accuracy; (b) Normalized Mutual Information; (c) Modularity.
Figure 9
Figure 9
The Q value of the nine algorithms on eight datasets.
Figure 10
Figure 10
The results of running ALMV algorithm on 20NGs and 100leaves datasets. (a) 20NGs-multiview; (b) 20NGs-view1; (c) 20NGs-view2; (d) 20NGs-view3; (e) 100leaves-multiview; (f) 100leaves-view1; (g) 100leaves-view2; (h) 100leaves-view3.
Figure 10
Figure 10
The results of running ALMV algorithm on 20NGs and 100leaves datasets. (a) 20NGs-multiview; (b) 20NGs-view1; (c) 20NGs-view2; (d) 20NGs-view3; (e) 100leaves-multiview; (f) 100leaves-view1; (g) 100leaves-view2; (h) 100leaves-view3.
Figure 11
Figure 11
The matrix diagram of running ALMV algorithm on 20NGs and 100leaves datasets. (a) 20NGS-multiview; (b) 20NGs-view1; (c) 20NGs-view2; (d) 20NGs-view3; (e) 100leaves-multiview; (f) 100leaves-view1; (g) 100leaves-view2; (h) 100leaves-view3.
See this image and copyright information in PMC

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