TY - GEN
T1 - Improving Togetherness Using Structural Entropy
AU - Zhang, Siyu
AU - Liu, Jiamou
AU - Liu, Yiwei
AU - Zhang, Zijian
AU - Khoussainov, Bakhadyr
N1 - Publisher Copyright:
© 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Social network
KW - Structural entropy
KW - Togetherness
UR - http://www.scopus.com/inward/record.url?scp=85127726461&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-99203-3_6
DO - 10.1007/978-3-030-99203-3_6
M3 - Conference contribution
AN - SCOPUS:85127726461
SN - 9783030992026
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 85
EP - 98
BT - Mobile Computing, Applications, and Services - 12th EAI International Conference, MobiCASE 2021, Proceedings
A2 - Deng, Shuiguang
A2 - Zomaya, Albert
A2 - Li, Ning
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th EAI International Conference on Mobile Computing, Applications and Services, MobiCASE 2021
Y2 - 13 November 2021 through 14 November 2021
ER -