Evolutionary Algorithm For Community Detection Research Articles

A Node Classification-Based Multiobjective Evolutionary Algorithm for Community Detection in Complex Networks

Multiobjective evolutionary algorithms (MOEAs) have been widely used in community detection in recent years. However, most of the existing MOEA-based ones adopted the same search strategies for all nodes and ignored the differences between the nodes. In fact, the nodes in a complex network have different structural characteristics and are of different importance during the search process of the community detection problem. To this end, in this article, a node classification-based search scheme is first proposed, where different kinds of nodes are searched in different ways. To be specific, the nodes in the network are classified into two types of nodes, candidate central (CC) nodes and noncentral (NC) nodes, by mapping the nodes into a structural similarity-based embedding space. The CC nodes are likely to be the centers of communities, and the rough structure can be searched quickly through activating the CC nodes. Then, the NC nodes are assigned to the communities with the activated central nodes. Based on the proposed scheme, a node classification-based MOEA named NCMOEA is then proposed. In NCMOEA, a mixed representation is designed to effectively encode the two different kinds of nodes. In addition, corresponding genetic operators are then suggested to search the two categories of nodes in different ways. Furthermore, an initialization strategy is also designed for initializing the population with high quality and good diversity. The experimental results on 15 real-world networks and several synthetic networks demonstrate the superiority of the proposed NCMOEA over nine representative algorithms for community detection.

An improved continuous-encoding-based multiobjective evolutionary algorithm for community detection in complex networks

An improved continuous-encoding-based multiobjective evolutionary algorithm for community detection in complex networks

A Two-Stage Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks

Community detection is a crucial research direction in the analysis of complex networks and has been shown to be an NP-hard problem (a problem that is at least as hard as the hardest problems in nondeterministic polynomial time). Multi-objective evolutionary algorithms (MOEAs) have demonstrated promising performance in community detection. Given that distinct crossover operators are suitable for various stages of algorithm evolution, we propose a two-stage algorithm that uses an individual similarity parameter to divide the algorithm into two stages. We employ appropriate crossover operators for each stage to achieve optimal performance. Additionally, a repair operation is applied to boundary-independent nodes during the second phase of the algorithm, resulting in improved community partitioning results. We assessed the effectiveness of the algorithm by measuring its performance on a synthetic network and four real-world network datasets. Compared to four existing competing methods, our algorithm achieves better accuracy and stability.

A new single-chromosome evolutionary algorithm for community detection in complex networks by combining content and structural information

Community detection is an important step in perceiving network structure and performance for complex network analysis. The rapid growth of network data in recent years has piqued the interest of many researchers in community detection. The majority of community detection methods only consider the network structure. Nonetheless, real-world network nodes may have some characteristics that can be useful for community detection. This study proposed a novel single-chromosome evolutionary algorithm with a distinctive architecture modification operator for community detection in complex networks using a combination of structural and content information. To this end, a novel virtual network was created by taking into account the structure and content of nodes, and communities were discovered for this network by optimizing the objective function (and using the combinatorial adjacency matrix instead of the structural adjacency matrix) in a series of steps. The nodes in this network were the same as the nodes in the main network; however, the links were developed based on similarities between nodes and their structural neighborhood. The proposed algorithm also included a method for sorting new nodes in order to determine the analysis order of nodes along with the local improvement of solution, as well as a new criterion, CS, for measuring the content similarity of nodes. The proposed algorithm was evaluated in real-networks and compared to various state-of-the-art and widely used methods. The Friedman rank algorithm was then used to rank the proposed algorithm and the existing methods using six real networks. According to the NMI criterion used in the Friedman rank test, the rank of the proposed algorithms increased by 96.8762%, 70.2693%, 26.0005%, 23.5294%, 46.5109%, and 23.5294% compared respectively with ASCD-ARC, BTLSC, Adapt-SA, PSB-PG, RSECD, and NEMBP, which have all been proposed in recent years.

A parallel multi-objective evolutionary algorithm for community detection in large-scale complex networks

Community detection in large-scale complex networks has recently received significant attention as the volume of available data is becoming larger. The use of evolutionary algorithms (EAs) for community detection in large-scale networks has gained considerable popularity because these algorithms are fairly effective in networks with a relatively small number of nodes. In this paper, we propose a parallel multi-objective EA, called PMOEA, for community detection in large-scale networks, where the communities associated with key network nodes are detected in parallel. Specifically, we develop a multi-objective and a single-objective EA. The former is used to detect the communities of a key node instead of all communities in the network. The latter obtains the communities in the entire network using the previously detected communities of each key node. The performance of the proposed method was verified on both large-scale synthetic benchmark networks and real-world networks. The results demonstrated the superiority of PMOEA over six EA-based and two non-EA-based community-detection algorithms for large-scale networks.

Determining Network Communities Based on Modular Density Optimization

Background:In today’s world, complex systems are conceptually observed in the form of network structure. Communities inherently existing in the networks have a recognizable elucidation in understanding the organization of networks. Community discovery in networks has grabbed the attention of researchers from multi-discipline. Community detection problem has been modeled as an optimization problem. In broad-spectrum, existing community detection algorithms have adopted modularity as the optimizing function. However, the modularity is not able to identify communities of smaller size as compared to the size of the network.Methods:This paper addresses the problem of the resolution limit posed by modularity. Modular density measure succeeds in countering the resolution limit problem. Finding network communities with maximum modular density is an NP-hard problem In this work, the discrete bat algorithm with modular density as the optimization function is recommended.Results:Experiments are conducted on three real-world datasets. For determining the consistency, ten independent runs of the proposed algorithm has been carried out. The experimental results show that our proposed algorithm produces high-quality community structure along with small size communities.Conclusion:The results are compared with traditional and evolutionary community detection algorithms. The final outcome shows the superiority of discrete bat algorithm with modular density as the optimization function with respect to number of communities, maximum modularity, and average modularity.

A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks

Community detection is a key aspect for understanding network structures and uncovers the underlying functions or characteristics of complex systems. A community usually refers to a set of nodes that are densely connected among themselves, but sparsely connected to the remaining nodes of the network. Detecting communities has been proved to be a NP-hard problem. Therefore, evolutionary based optimization approaches can be used to solve it. But a primary challenge for them is the higher computational complexity when dealing with large scale networks. In this respect, a COMpression based Multi-Objective Evolutionary Algorithm with Decomposition (Com-MOEA/D) for community detection is proposed where the network is first compressed to a much more smaller scale by exploring network topologies. After that, a framework of multi-objective evolutionary algorithm based on decomposition is applied, in which a local information based genetic operator is proposed to speed up the convergence and improve the accuracy of the Com-MOEA/D algorithm. Experimental results on both real world and synthetic networks show the superiority of the proposed method over several state-of-the-art community detection algorithms.

A Network Reduction-Based Multiobjective Evolutionary Algorithm for Community Detection in Large-Scale Complex Networks

Evolutionary algorithms have been demonstrated to be very competitive in the community detection for complex networks. They, however, show poor scalability to large-scale networks due to the exponential increase of search space. In this paper, we suggest a network reduction-based multiobjective evolutionary algorithm for community detection in large-scale networks, where the size of the networks is recursively reduced as the evolution proceeds. In each reduction of the network, the local communities found by the elite individuals in the population are identified as nodes of the reduced network for further evolution, thereby considerably reducing the search space. A local community repairing strategy is also suggested to correct the misidentified nodes after each network reduction during the evolution. Experimental results on synthetic and real-world networks demonstrate the superiority of the proposed algorithm over several state-of-the-art community detection algorithms for large-scale networks, in terms of both computational efficiency and detection performance.

Quantum inspired evolutionary algorithm for community detection in complex networks

Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance.

A local information based multi-objective evolutionary algorithm for community detection in complex networks

Due to the important role in analyzing the structure and function of complex networks, community detection has attracted increasing attention in the past years. Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in community detection and, in this paper, we continue this research line by further exploring the potential of MOEAs in detecting communities. To be specific, a local information based MOEA, termed LMOEA, is proposed for community detection, where an individual updating strategy is suggested to improve the quality of community detection. Considering that a network often contains some local communities which are easily detected in the early evolutions, the proposed strategy utilizes these local communities found by individuals to guide the search in the following generations. The effectiveness of the proposed LMOEA is verified by comparing it with several existing evolutionary algorithms for community detection on both synthetic and real-world networks. Experimental results demonstrate the competitiveness of the proposed LMOEA for community detection in complex networks.

Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks

Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.