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A theoretical and computational approach for the community detection problem in networks

Abstract

The community detection problem aims at finding groups of nodes in a graph in such a way that the nodes within communities (or clusters) share high connectivity. The objective of this problem is to find partitions in a network or graph. One of its applications consists in determining communities in social networks in order to explain the behavior of groups of individuals taking their iterations into account. This problem can be defined using different objective functions and connectivity measures. In the last decades, the community detection problem was strengthened with the arising of a novel graph partitioning evaluation measure, the text it {modularity}. Since then, many studies that optimize (maximize) this measure in order to find partitions in graphs were proposed. In some of these studies, it was observed that, for some types of networks, the communities found by the modularity maximization-based algorithms has a weak nature. Therefore, a theoretical study to better explain the behavior of the partitions found through its optimization became necessary. In this project, the head researcher intend to approach two topics involving the modularity maximization problem: its theoretical study using matroid theory; and the development of algorithms based on its spectral relaxation, hybridizing them with metaheuristics. In sum up, this project consists on the continuation of the proposal referred as Proc. no. 2009/16603-0 regarding the pos-doctorate financial support. The scholarship started on March 2010 and was cancelled on October 2010 (due to the new position of the researcher as a professor of UNIFESP). (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MAXIMO, VINICIUS R.; NASCIMENTO, MARIA C. V.; CARVALHO, ANDRE C. P. L. F. Intelligent-guided adaptive search for the maximum covering location problem. Computers & Operations Research, v. 78, p. 129-137, FEB 2017. Web of Science Citations: 6.

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