Abstract
This study investigates one of the major challenges in analysis of social networks: the identification of key nodes or important actors. There are numerous algorithms and approaches to locating and ranking nodes that may be critical in processes such as influence and diffusion. Most of the current algorithms consider a single criterion like the degree or page-rank of the nodes. However, many real world applications with large networks that display local sub-structure no single criterion may be adequate. We briefly discuss some single criteria that are often used to assess how node importance. Then a multiple-criteria decision-making method algorithm, TOPSIS, is presented. The proposed algorithm is examined on three datasets with varying size and sub-structure. Comparison of the results to those of other ranking algorithms such as PageRank indicate the ability of the suggested multi-criteria method to unambiguously rank nodes while remaining sensitive to the multiple ways in which a node may be “important”.
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Mesgari, I., Kermani, M.A.M.A., Hanneman, R., Aliahmadi, A. (2015). Identifying Key Nodes in Social Networks Using Multi-Criteria Decision-Making Tools. In: Mugnolo, D. (eds) Mathematical Technology of Networks. Springer Proceedings in Mathematics & Statistics, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-319-16619-3_10
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DOI: https://doi.org/10.1007/978-3-319-16619-3_10
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