Robustness in network community detection under links weights uncertainties

J. E. Ramirez-Marquez, C. M. Rocco, J. Moronta, D. Gama Dessavre

    Research output: Contribution to journalArticlepeer-review

    9 Scopus citations

    Abstract

    In network analysis, a community can be defined as a group of nodes of a network (or clusters) that are densely interconnected with each other but only sparsely connected with the rest of the network. Several algorithms have been used to obtain a convenient partition allowing extracting the communities in a given network, based on their topology and, possibly, the weights of links. These weights usually represent specific characteristics for example: distance, reactance, reliability. Even if the optimum partitions could be derived, there are uncertainties associated to the network parameters that affect the network partition. In this paper, the authors extend a previous approach for assessing the effects of weight uncertainties on community structures and propose a global approach for (a) understanding the global similarity among the partitions; (b) analyzing the robustness of the communities derived without uncertainty; and (c) quantifying the robustness of the inter-community links. To this aim an uncertainty propagation analysis, based on the Monte Carlo technique is proposed. The approach is illustrated through analyzing the topology of an electric power system.

    Original languageEnglish
    Pages (from-to)88-95
    Number of pages8
    JournalReliability Engineering and System Safety
    Volume153
    DOIs
    StatePublished - 1 Sep 2016

    Keywords

    • Community detection
    • networks
    • robustness
    • similarity
    • uncertainty

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