Community Detection in Social Network Based on Node Influence Expansion
Community structure is a type of node aggregate that exists on both a micro and macro scale, and it is critical to fully comprehend the behavior and law of social network users.Traditional community detection approaches presume that all nodes in a network have the same status, neglecting the infl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Harbin University of Science and Technology Publications
2023-06-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2209 |
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| Summary: | Community structure is a type of node aggregate that exists on both a micro and macro scale, and it is critical to fully comprehend the behavior and law of social network users.Traditional community detection approaches presume that all nodes in a network have the same status, neglecting the influence and function of node influence in community formation.To solve this problem, a community detection method based on node influence expansion is proposed. To begin with, the local influence of nodes is computed using the Monte Carlo approximation. Then, a new eccentricity calculation approach is provided to screen bridge nodes and improve seed quality. Finally, to finish the process of community detection, dynamic programming is employed for seed expansion.The results of the experiments reveal that community detection based on node influence expansion can effectively discover the community structure with small granularity and has certain performance advantages in modularity, D-score, and other indicators. |
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| ISSN: | 1007-2683 |