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Heterogeneous Influence Maximization Through Community Detection in Social Networks

Heterogeneous Influence Maximization Through Community Detection in Social Networks

Jaya Krishna Raguru, Devi Prasad Sharma
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 14
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799860297|DOI: 10.4018/IJACI.2021100107
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MLA

Raguru, Jaya Krishna, and Devi Prasad Sharma. "Heterogeneous Influence Maximization Through Community Detection in Social Networks." IJACI vol.12, no.4 2021: pp.118-131. http://doi.org/10.4018/IJACI.2021100107

APA

Raguru, J. K. & Sharma, D. P. (2021). Heterogeneous Influence Maximization Through Community Detection in Social Networks. International Journal of Ambient Computing and Intelligence (IJACI), 12(4), 118-131. http://doi.org/10.4018/IJACI.2021100107

Chicago

Raguru, Jaya Krishna, and Devi Prasad Sharma. "Heterogeneous Influence Maximization Through Community Detection in Social Networks," International Journal of Ambient Computing and Intelligence (IJACI) 12, no.4: 118-131. http://doi.org/10.4018/IJACI.2021100107

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Abstract

The problem of identifying a seed set composed of K nodes that increase influence spread over a social network is known as influence maximization (IM). Past works showed this problem to be NP-hard and an optimal solution to this problem using greedy algorithms achieved only 63% of spread. However, this approach is expensive and suffered from performance issues like high computational cost. Furthermore, in a network with communities, IM spread is not always certain. In this paper, heterogeneous influence maximization through community detection (HIMCD) algorithm is proposed. This approach addresses initial seed nodes selection in communities using various centrality measures, and these seed nodes act as sources for influence spread. A parallel influence maximization is applied with the aid of seed node set contained in each group. In this approach, graph is partitioned and IM computations are done in a distributed manner. Extensive experiments with two real-world datasets reveals that HCDIM achieves substantial performance improvement over state-of-the-art techniques.

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