Influential node ranking in social networks based on neighborhood diversity

Zareie, A, Sheikhahmadi, A and Jalili, M 2019, 'Influential node ranking in social networks based on neighborhood diversity', Future Generation Computer Systems, vol. 94, pp. 120-129.

Document type: Journal Article
Collection: Journal Articles

Title Influential node ranking in social networks based on neighborhood diversity
Author(s) Zareie, A
Sheikhahmadi, A
Jalili, M
Year 2019
Journal name Future Generation Computer Systems
Volume number 94
Start page 120
End page 129
Total pages 10
Publisher Elsevier BV * North-Holland
Abstract Social networks have significant role in distribution of ideas and advertisement. Discovering the most influential nodes has been a hot topic in the field of social networks analysis and mining. This manuscript proposes novel algorithms for this purpose based on neighborhood diversity. We introduce two new influential node ranking algorithms that use diversity of the neighbors of each node in order to obtain its ranking value. They are applied on a number of real-world networks and compared with state-of-the-art algorithms. Our experimental results reveal effectiveness of the proposed algorithms.
Subject Dynamical Systems in Applications
Complex Physical Systems
Keyword(s) Influential nodes
Node centrality
Entropy divergence
Social networks analysis and mining
SIR model
DOI - identifier 10.1016/j.future.2018.11.023
Copyright notice © 2018 Elsevier B.V
ISSN 0167-739X
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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