A new centrality measure for influence maximization in social networks

Abstract

The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose a centrality measure for independent cascade model, which is based on diffusion probability (or propagation probability) and degree centrality. We use (i) centrality based heuristics with the proposed centrality measure to get k influential individuals. We have also found the same using (ii) high degree heuristics and (iii) degree discount heuristics. A Monte-Carlo simulation has been conducted with top k-nodes found through different methods. The result of simulation indicates, k nodes obtained through (i) significantly outperform those obtain by (ii) and (iii). We further verify the differences statistically using T-Test and found the minimum significance level (p-value) when k∈ textgreater ∈5 is 0.022 compare with (ii) and 0.015 when comparing with (iii) for twitter data. © 2011 Springer-Verlag Berlin Heidelberg.

Publication
Proc. of 4th International Conference on Pattern Recognition and Machine Intelligence (PReMI’11)
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Suman Kundu
Assistant Professor of Computer Science and Engineering

My research interests include social network analysis, network data science, granular computing, soft computing, fuzzy and rough sets.

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