Operations Research
Detecting critical nodes in sparse graphs
Computers and Operations Research
Latency-Bounded Minimum Influential Node Selection in Social Networks
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
Linear and quadratic programming approaches for the general graph partitioning problem
Journal of Global Optimization
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
On the two-stage stochastic graph partitioning problem
COCOA'11 Proceedings of the 5th international conference on Combinatorial optimization and applications
Computers and Operations Research
Robust optimization of graph partitioning involving interval uncertainty
Theoretical Computer Science
Branch and cut algorithms for detecting critical nodes in undirected graphs
Computational Optimization and Applications
Weighted graph-based methods for identifying the most influential actors in trust social networks
International Journal of Networking and Virtual Organisations
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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 isminimized. 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.