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Community discovery algorithm of complex network attention model
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DOI：10.1007/s13042-021-01471-w.png)
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摘要
En 中文
In terms of understanding the structure of complex networks or the functional characteristics of complex networks, community discovery is of great significance. This paper uses the attention model to combine the second-order neighbor similarity matrix with the modularity matrix, extracts relatively more comprehensive complex network feature information from multiple angles for network division. Firstly, perform complex network preprocessing, and perform division preprocessing according to the value of the attention similarity matrix. Secondly, complete the merger of the community game according to the connection strength between the two different communities. By comparing with other algorithms on computer-generated networks and real-world networks, it is proved that this algorithm has obtained good results in terms of the number of communities, running time, normalized mutual information and modularity.
Keyword:
Feature extraction
Attention model
Community discovery
Community game merge
Complex network
期刊
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2.7
论文数:
3.2K
被引数:
5.6K
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引用论文
From Louvain to Leiden: guaranteeing well-connected communities从鲁汶到莱顿: 保证良好连接的社区.png)
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