TY - JOUR
T1 - Evolutionary community discovery in dynamic social networks via resistance distance
AU - Li, Weimin
AU - Zhu, Heng
AU - Li, Shaohua
AU - Wang, Hao
AU - Dai, Hongning
AU - Wang, Can
AU - Jin, Qun
N1 - Funding Information:
This work was supported by the National Key R&D Program for China (No. 2017YFE0117500).
Publisher Copyright:
© 2020 Elsevier Ltd.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - Traditional social community discovery methods concentrate mainly on static social networks, but the analysis of dynamic networks is a prerequisite for real-time and personalized social services. Through the study of community changes, the community structure in a dynamic network can be tracked over time, which helps in the mining of dynamic network information. In this paper, we propose a method of tracking dynamic community evolution that is based on resistance distance. Specifically, we model the time-varying features of dynamic networks using the convergence of a resistance-based distance. In our model, the heterogeneity of neighboring nodes can be obtained in the local topology of nodes by analyzing the resistance distance between nodes. We design a community discovery algorithm that essentially discovers community structures on dynamic networks by identifying the so-called core node. During the process of community evolution analysis, both the dynamic contribution of ordinary nodes and core nodes in each community are considered. In addition, to avoid the inclusion of spurious communities in the community structure, we define the notion of noise community and account for it in our algorithm. Experimental results show that the method proposed in this paper can yield better accuracy than other existing methods.
AB - Traditional social community discovery methods concentrate mainly on static social networks, but the analysis of dynamic networks is a prerequisite for real-time and personalized social services. Through the study of community changes, the community structure in a dynamic network can be tracked over time, which helps in the mining of dynamic network information. In this paper, we propose a method of tracking dynamic community evolution that is based on resistance distance. Specifically, we model the time-varying features of dynamic networks using the convergence of a resistance-based distance. In our model, the heterogeneity of neighboring nodes can be obtained in the local topology of nodes by analyzing the resistance distance between nodes. We design a community discovery algorithm that essentially discovers community structures on dynamic networks by identifying the so-called core node. During the process of community evolution analysis, both the dynamic contribution of ordinary nodes and core nodes in each community are considered. In addition, to avoid the inclusion of spurious communities in the community structure, we define the notion of noise community and account for it in our algorithm. Experimental results show that the method proposed in this paper can yield better accuracy than other existing methods.
KW - Community discovery
KW - Community evolution
KW - Dynamic social networks
KW - Resistance distance
UR - http://www.scopus.com/inward/record.url?scp=85100001315&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2020.114536
DO - 10.1016/j.eswa.2020.114536
M3 - Journal article
AN - SCOPUS:85100001315
SN - 0957-4174
VL - 171
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 114536
ER -