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Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13–14, 2021, Proceedings

Research Article

Improving Togetherness Using Structural Entropy

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  • @INPROCEEDINGS{10.1007/978-3-030-99203-3_6,
        author={Siyu Zhang and Jiamou Liu and Yiwei Liu and Zijian Zhang and Bakhadyr Khoussainov},
        title={Improving Togetherness Using Structural Entropy},
        proceedings={Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13--14, 2021, Proceedings},
        proceedings_a={MOBICASE},
        year={2022},
        month={3},
        keywords={Togetherness Social network Structural entropy},
        doi={10.1007/978-3-030-99203-3_6}
    }
    
  • Siyu Zhang
    Jiamou Liu
    Yiwei Liu
    Zijian Zhang
    Bakhadyr Khoussainov
    Year: 2022
    Improving Togetherness Using Structural Entropy
    MOBICASE
    Springer
    DOI: 10.1007/978-3-030-99203-3_6
Siyu Zhang1, Jiamou Liu2, Yiwei Liu1, Zijian Zhang3,*, Bakhadyr Khoussainov4
  • 1: School of Computer Science and Technology
  • 2: School of Computer Science
  • 3: School of Cyberspace Science and Technology
  • 4: School of Computer Science and Engineering
*Contact email: zhangzijian@bit.edu.cn

Abstract

A major theme in the study of social dynamics is the formation of a community structure on a social network, i.e., the network contains several densely connected region that are sparsely linked between each other. In this paper, we investigate the network integration process in which edges are added to dissolve the communities into a single unified network. In particular, we study the following problem which we refer to as togetherness improvement: given two communities in a network, iteratively establish new edges between the communities so that they appear as a single community in the network. Towards an effective strategy for this process, we employ tools from structural information theory. The aim here is to capture the inherent amount of structural information that is encoded in a community, thereby identifying the edge to establish which will maximize the information of the combined community. Based on this principle, we design an efficient algorithm that iteratively establish edges. Experimental results validate the effectiveness of our algorithm for network integration compared to existing benchmarks.

Keywords
Togetherness Social network Structural entropy
Published
2022-03-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99203-3_6
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