Name
Abstract | ||
|---|---|---|
In the field of virtual community detection in online social networks, most of existing methods often detect communities from a single perspective, and ignore the influence of related characteristics of the networks on community detection. All these reduce the interpretability and accuracy of the community partition results. In order to resolve this problem, a virtual community detection model framework for online social networks is proposed. The model framework considers three key factors that affect the community detection results: the structural characteristics, the attribute information and the nodes' influence levels of the network. The proposed model is not only a mapping of existing community detection models, but also a reference for designing more future models for community detection methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/DSC.2018.00018 | 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) |
Keywords | Field | DocType |
community detection,online social network,model framework | Interpretability,Social network,Computer science,Artificial intelligence,Cluster analysis,Hidden Markov model,Machine learning,Virtual community | Conference |
ISBN | Citations | PageRank |
978-1-5386-4211-5 | 0 | 0.34 |
References | Authors | |
12 | 6 | |