Research Article
Recognition of Self-organized Aggregation Behavior in Social Networks Based on Ant Colony Algorithm
@INPROCEEDINGS{10.1007/978-3-031-28867-8_7, author={Nan Hu and Hongjian Li}, title={Recognition of Self-organized Aggregation Behavior in Social Networks Based on Ant Colony Algorithm}, proceedings={Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2023}, month={3}, keywords={Ant colony algorithm Social network Self-organization Aggregation behavior Behavior recognition User characteristics}, doi={10.1007/978-3-031-28867-8_7} }- Nan Hu
Hongjian Li
Year: 2023
Recognition of Self-organized Aggregation Behavior in Social Networks Based on Ant Colony Algorithm
ADHIP PART 2
Springer
DOI: 10.1007/978-3-031-28867-8_7
Abstract
In order to effectively detect the real network community structure and improve the accuracy of user stage partitioning to the corresponding self-organized community, a self-organized clustering behavior recognition method based on ant colony algorithm is proposed. According to user’s individual attribute and collaborative attribute, the node with high aggregation coefficient under user’s knowledge quality scale is chosen as the core to construct social network aggregation behavior community. The evolutionary types of group trajectory are divided into seven types. Ant colony algorithm is used to track the group trajectory. Abstract tagged basic events from user attributes, establish recognition model to identify abnormal behavior, and realize self-organized aggregation behavior recognition in social network. Experimental results show that the self-organized aggregation recognition method based on ant colony algorithm can get more reasonable group structure, better quality of community partition, and improve the accuracy of user stage partition to the corresponding self-organized community.
