Abstract
Community detection is an important problem in social network analysis. Most of the existing research on this topic is mainly based on single network. However, a single network cannot fully reflect the entire social relationship of an individual because of the diversity of social networks. To discover the community structures from multiple networks, a community detection algorithm based on the joint representation of multi-granular networks is proposed in this paper. First, the nodes in each network are embedded to obtain the corresponding vectors. Second, a joint representation of multi-granular networks is formed after the anchor nodes in each network interact to complement their information. Finally, an improved density peak algorithm called Center Density Peak algorithm (CDP) is proposed. Experiments on synthetic and real-world datasets show that the rich structural information of multi-granular networks can improve the accuracy of community detection.
Highlights
A network is constituted by a set of entities and the relationships between them
In order to solve the above problems, this paper proposes a community detection algorithm based on the joint representation of multi-granular networks
The main contributions of this paper can be summarized as follows: 1. A community detection algorithm based on the joint representation of multi-granular networks is proposed, in which the information of multiple networks can be fully employed to overcome the shortcoming of the information incompleteness of a single network
Summary
A network is constituted by a set of entities and the relationships between them. Generally, entities within the same community have a stronger relationship that represents the same hobbies or closer connections. Individuals often join multiple social networks for different purposes (e.g. friends, colleagues, relatives, etc.) This may result in the loss of structure information about individual and an incomplete structure of the detected communities when a single network is used to calculate an individual’s social relationship. Zhang et al.: Community Detection Based on Joint Representation of Multi-Granular Networks. In order to solve the above problems, this paper proposes a community detection algorithm based on the joint representation of multi-granular networks. A community detection algorithm based on the joint representation of multi-granular networks is proposed, in which the information of multiple networks can be fully employed to overcome the shortcoming of the information incompleteness of a single network.
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