An incremental approach to update influential nodes in dynamic social networks

Procedia Computer Science ◽

10.1016/j.procs.2020.09.073 ◽

2020 ◽

Vol 176 ◽

pp. 781-790

Author(s):

Nesrine Hafiene ◽

Wafa Karoui ◽

Lotfi Ben Romdhane

Keyword(s):

Social Networks ◽

Incremental Approach ◽

Dynamic Social Networks ◽

Influential Nodes

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SIMiner: A Stigmergy-Based Model for Mining Influential Nodes in Dynamic Social Networks

IEEE Transactions on Big Data ◽

10.1109/tbdata.2018.2824826 ◽

2019 ◽

Vol 5 (2) ◽

pp. 223-237 ◽

Cited By ~ 1

Author(s):

Weihua Li ◽

Quan Bai ◽

Minjie Zhang

Keyword(s):

Social Networks ◽

Dynamic Social Networks ◽

Influential Nodes

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Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

International Journal of Modern Physics B ◽

10.1142/s0217979216500922 ◽

2016 ◽

Vol 30 (16) ◽

pp. 1650092 ◽

Cited By ~ 5

Author(s):

Tingting Wang ◽

Weidi Dai ◽

Pengfei Jiao ◽

Wenjun Wang

Keyword(s):

Social Networks ◽

Real World ◽

Dynamic Networks ◽

Dynamic Network ◽

Community Structures ◽

Block Model ◽

Real World Data ◽

Stochastic Block Model ◽

Dynamic Social Networks ◽

Influential Nodes

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.

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