Genetic clustering of social networks using random walks

Firat, Aykut; Chatterjee, Sanjit; Yilmaz, Mustafa R.

doi:10.1016/j.csda.2007.01.010

Cited by 49 publications

(20 citation statements)

References 10 publications

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“…al. [25] Regarding approaches to community detection based on Genetic Algorithms, only few proposals can be found in the literature [30,31,8]. None of them, however, contemplate the case of overlapping communities.…”

Section: Related Workmentioning

confidence: 99%

Overlapped community detection in complex networks

Pizzuti

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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Extracting and understanding community structure in complex networks is one of the most intensively investigated problems in recent years. In this paper we propose a genetic based approach to discover overlapping communities. The algorithm optimizes a fitness function able to identify densely connected groups of nodes by employing it on the line graph corresponding to the graph modelling the network. The method generates a division of the network in a number of groups in an unsupervised way. This number is automatically determined by the optimal value of the fitness function. Experiments on synthetic and real life networks show the capability of the method to successfully detect the network structure.

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Section: Related Workmentioning

confidence: 99%

Overlapped community detection in complex networks

Pizzuti

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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“…Rather than explicitly optimizing the Q metric as a fitness function, Pizzuti [8] attempts to identify densely connected groups of nodes separated by sparse connections using a fitness function called community score to identify the community structure of a network. Firat et al [9] do not use the Q metric for fitness either; instead they use a random walk distance measure between cluster centers as nodes with the number of clusters decided a priori.…”

Section: B Use Of Gas For Community Detectionmentioning

confidence: 99%

Detecting anomalies in spatiotemporal data using genetic algorithms with fuzzy community membership

Wilson

Harding

Hoeber

et al. 2010

2010 10th International Conference on Intelligent Systems Design and Applications

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A genetic algorithm is combined with two variants of the modularity (Q) network analysis metric to examine a substantial amount fisheries catch data. The data set produces one of the largest networks evaluated to date by genetic algorithms applied to network community analysis. Rather than using GA to decide community structure that simply maximizes modularity of a network, as is typical, we use two fuzzy community membership functions applied to natural temporal divisions in the network so the GA is used to find interesting areas of the search space through maximization of modularity. The work examines the performance of the genetic algorithm against simulated annealing using both types of fuzzy community membership functions. The algorithms are used in an existing visualization software prototype, where the solutions are evaluated by a fisheries expert.

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“…Among the function optimization methods, evolutionary algorithm (EA) has been widely used as an important tool [7][8][9][10]. Pizzuti [8] proposed a method based on a genetic algorithm (GA) to optimize an objective function called community score (CS) in order to explore the community structure in social networks.…”

Section: Introductionmentioning

confidence: 99%