Research Article
Improving Togetherness Using Structural Entropy
@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
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.
