Multi-objective NSGA-II based community detection using dynamical evolution social network

Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations

Detection of Overlapping Communities in Social Network

Community detection in a social network is an emerging issue in the study of network system as it helps to realize the overall network structure in depth. Communities are the natural partition of network nodes into subgroups where nodes within the subgroup are densely connected but between the subgroups connections are sparser. Real world networks, including social networks have been found to partition themselves naturally into communities. A member of a social network can be part of more than one group or community. As a member of a social network can be overlapped between more than one group, overlapping community detection technique need to be considered in order to identify the overlapping nodes. This topic of research has many applications in various fields like biology, social sciences, physics etc. In literature, most of the proposed community detection approaches are able to detect only disjoint communities. Recently few algorithms has been emerged which are capable of discovering overlapping communities. In this work two different types of algorithms have been proposed which efficiently detect overlapping communities. A novel approach has been introduced which overcomes the shortfalls of clique percolation method, an overlapping community detection algorithm mostly used in this area. Another algorithm which is based on Genetic Algorithm is also used to discover overlapping communities. Modularity measure is generally used to determine the quality of communities for the particular network. The quality of the communities detected by the algorithms is measured by several different overlapping modularity measures. Standard real world networks used as benchmark for community detection have been used to judge the algorithms

Efficient Reduced-Bias Genetic Algorithm (ERBGA) for Generic Community Detection Objectives

Community structure identification has been an important research area for biology, physics, information systems, and social sciences for studying properties of networks representing complex relationships. Lately, Genetic Algorithms (GAs) are being utilized for community detection. GAs are machine-learning methods that mimic natural selection. However, previous approaches suffer from some deficiencies: redundant representation and linearity assumption, that we will try to address. in. The algorithm presented here is a novel framework that addresses both of these above issues. This algorithm is also flexible as it is easily adapted to any given mathematical objective. Additionally, our approach doesn’t require prior information about the number of true communities in the network. Overall, our efficient approach holds potential for sifting out communities representing complex relationships in networks of interest across different domains

Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively)

Bi-Objective Community Detection (BOCD) in Networks using Genetic Algorithm

A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a Bi-Objective Genetic Algorithm for community detection which maximizes modularity and community score. Then the results obtained for both benchmark and real life data sets are compared with other algorithms using the modularity and MNI performance metrics. The results show that the BOCD algorithm is capable of successfully detecting community structure in both real life and synthetic datasets, as well as improving upon the performance of previous techniques.Comment: 11 pages, 3 Figures, 3 Tables. arXiv admin note: substantial text overlap with arXiv:0906.061

Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Objectives

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by introducing redundancies. They also utilize a crossover operator which imposes a linear ordering that is not suitable for community detection. The algorithm presented here is a framework to detect communities for complex biological networks that removes both redundancies and linearity. We also introduce a novel operator, named Gene Repair. This algorithm is unique as it is a flexible community detection technique aimed at maximizing the value of any given mathematical objective for the network. We reduce the memory requirements by representing chromosomes as a 3-dimensional bit array. Furthermore, in order to increase diversity while retaining promising chromosomes, we use natural selection process based on tournament selection with elitism. Additionally, our approach doesn’t require prior information about the number of true communities in the network. We apply our novel algorithm to benchmark datasets and also to a network representing a large cohort of AD cases and controls. By utilizing this efficient and flexible implementation that is cognizant of characteristics for networks representing complex disease genetics, we sift out communities representing patterns of interacting genetic variants that are associated with this enigmatic disease