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• W762002324 abstract "We are surrounded by, immersed in, and have to deal regularly with many complex networks of various kinds and different origins. It is enough to mention computer or information networks, biological networks, and social networks of many types. No wonder that studies of complex networks focus so much attention of scientific communities of different subjects and backgrounds [1–4]. Networks can be, and in fact are, effectively modeled within graph theory, although such fundamental concepts as vertices (nodes) and edges (links) have to be always defined anew within the realm of particular application areas we are investigating. When studying such systems we are interested in both the structure of a network and in dynamical processes [5]. One of the important problems in studies of complex networks, especially if we deal with social networks, is finding out some (if any) underlying sub-structures. Searching for such a special group on nodes that, roughly speaking, has more connections inside itself than with the rest of the network, is often called the community detection problem. This problem has been extensively investigated over the last few years and many algorithms of different kinds and levels of sophistication have been developed, criticized, tested, and applied to various situations [6–11]. In this paper we focus our attention on one of possible approaches, namely on genetic algorithms. They form a set of procedures based on natural selection mechanisms and genetics and aim at finding exact or approximately solutions to optimization problems [12]. There are many versions of genetic algorithms developed for the task of community detection and here we concentrate on a very promising one proposed quite recently by Pizzuti [13]." @default.
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• W762002324 date "2010-04-01" @default.
• W762002324 modified "2023-10-18" @default.
• W762002324 title "Genetic Algorithms Approach to Community Detection" @default.
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• W762002324 doi "https://doi.org/10.12693/aphyspola.117.703" @default.
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