Research Article
Community Centrality-Based Greedy Approach for Identifying Top- Influencers in Social Networks
@INPROCEEDINGS{10.1007/978-3-319-29236-6_15, author={Bundit Manaskasemsak and Nattawut Dejkajonwuth and Arnon Rungsawang}, title={Community Centrality-Based Greedy Approach for Identifying Top- Influencers in Social Networks}, proceedings={Context-Aware Systems and Applications. 4th International Conference, ICCASA 2015, Vung Tau, Vietnam, November 26-27, 2015, Revised Selected Papers}, proceedings_a={ICCASA}, year={2016}, month={4}, keywords={Social network Community detection Node centrality Influence maximization Influencer}, doi={10.1007/978-3-319-29236-6_15} }- Bundit Manaskasemsak
Nattawut Dejkajonwuth
Arnon Rungsawang
Year: 2016
Community Centrality-Based Greedy Approach for Identifying Top- Influencers in Social Networks
ICCASA
Springer
DOI: 10.1007/978-3-319-29236-6_15
Abstract
Online social network today is an effective media to share and disperse tons of information, especially for advertizing and marketing. However, with limited budgets, commercial companies make hard efforts to determine a set of source persons who can highly diffuse information of their products, implying that more benefits will be received. In this paper, we propose an algorithm, called community centrality-based greedy algorithm, for the problem of finding top- influencers in social networks. The algorithm is composed of four main processes. First, a social network is partitioned into communities using the Markov clustering algorithm. Second, nodes with highest centrality values are extracted from each community. Third, some communities are combined; and last, top- influencers are determined from a set of highest centrality nodes based on the independent cascade model. We conduct experiments on a publicly available Higgs Twitter dataset. Experimental results show that the proposed algorithm executes much faster than the state-of-the-art greedy one, while still maximized nearly the same influence spread.
