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- W1567877685 abstract "The graph partitioning problem (GPP) consists of partitioning the vertex set of a graph into several disjoint subsets so that the sum of weights of the edges between the disjoint subsets is minimized. The critical node problem (CNP) is to detect a set of vertices in a graph whose deletion results in the graph having the minimum pairwise connectivity between the remaining vertices. Both GPP and CNP find many applications in identification of community structures or influential individuals in social networks, telecommunication networks, and supply chain networks. In this paper, we use integer programming to formulate GPP and CNP. In several practice cases, we have networks with uncertain weights of links. Some times, these uncertainties have no information of probability distribution. We use robust optimization models of GPP and CNP to formulate the community structures or influential individuals in such networks." @default.
- W1567877685 created "2016-06-24" @default.
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- W1567877685 date "2010-01-01" @default.
- W1567877685 modified "2023-10-05" @default.
- W1567877685 title "Robust Optimization of Graph Partitioning and Critical Node Detection in Analyzing Networks" @default.
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- W1567877685 doi "https://doi.org/10.1007/978-3-642-17458-2_15" @default.
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