Detecting Community Structures in Social Networks by Graph Sparsification

Author(s):  
Partha Basuchowdhuri ◽  
Satyaki Sikdar ◽  
Sonu Shreshtha ◽  
Subhashis Majumder
Africa ◽  
2000 ◽  
Vol 70 (3) ◽  
pp. 422-441 ◽  
Author(s):  
Douglas Anthony

AbstractBefore the civil war, conversion to Islam for Igbo men resident in the predominantly Hausa city of Kano in northern Nigeria usually meant becoming Hausa. More recent converts, however, have retained their Igbo identity and created an organisation, the Igbo Muslim Community. Three case studies from the first group detail the process and criteria of becoming Hausa, including immersion in Hausa economic and social networks; three case studies from the second group demonstrate that, while Hausa-centred networks remain important, converts have worked to construct new, Igbo-centred support structures. The watershed in the changing relationship between religious and ethnic affiliation for Igbo converts is the end of the war in 1970 and resultant changes in Igbo perceptions of Muslims, and changes in Igbo community structures.


2016 ◽  
Vol 30 (16) ◽  
pp. 1650092 ◽  
Author(s):  
Tingting Wang ◽  
Weidi Dai ◽  
Pengfei Jiao ◽  
Wenjun Wang

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.


2011 ◽  
Vol 30 (3) ◽  
pp. 1061-1070 ◽  
Author(s):  
Khairi Reda ◽  
Chayant Tantipathananandh ◽  
Andrew Johnson ◽  
Jason Leigh ◽  
Tanya Berger-Wolf

2018 ◽  
Vol 59 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Partha Basuchowdhuri ◽  
Satyaki Sikdar ◽  
Varsha Nagarajan ◽  
Khusbu Mishra ◽  
Surabhi Gupta ◽  
...  

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