A multiobjective genetic algorithm to uncover community structure in complex network is proposed. The algorithm optimizes two objective functions able to identify densely connected groups of nodes having sparse inter-connections. The method generates a set of network divisions at different hierarchical levels in which solutions at deeper levels, consisting of a higher number of modules, are contained in solutions having a lower number of communities. The number of modules is automatically determined by the better tradeoff values of the objective functions. Experiments on synthetic and real life networks show that the algorithm successfully detects the network structure and it is competitive with state-of-the-art approaches.

A Multiobjective Genetic Algorithm to Find Communities in Complex Networks

Studying the evolutionary community structure in complex networks is crucial for uncovering the links between structures and functions of a given community. Most contemporary community detection algorithms employs single optimization criteria (i.e.., modularity), which may not be adequate to represent the structures in complex networks. We suggest community detection process as a Multi-objective Optimization Problem (MOP) for investigating the community structures in complex networks. To overcome the limitations of the community detection problem, we propose a new multi-objective optimization algorithm based on enhanced firefly algorithm so that a set of non-dominated (Pareto-optimal) solutions can be achieved. In our proposed algorithm, a new tuning parameter based on a chaotic mechanism and novel self-adaptive probabilistic mutation strategies are used to improve the overall performance of the algorithm. The experimental results on synthetic and real world complex networks suggest that the multi-objective community detection algorithm provides useful paradigm for discovering overlapping community structures robustly.

Community Detection in Complex Networks: Multi-objective Enhanced Firefly Algorithm

In today’s world, the interconnections among objects in many domains are often modeled as networks, with nodes representing the objects and edges the existing relationships among them. A key feature of complex networks is the tendency of entities to group together to form communities. The detection of communities has been receiving a great deal of interest by researchers. In fact, the knowledge of how objects organize allows a better understanding of a network, and gives a deeper insight of interesting characteristics, that could not be caught if considering the network as a whole. In the last decade, evolutionary computation techniques have given a significant contribution in this context. The aim of this paper is to present the approaches based on evolutionary computation to uncover community structure. Especially, the representation schemes with the genetic operators apt for them are described, and the most popular fitness functions employed by the methods are discussed. The survey covers the most recent proposals optimizing either a single objective or multiple objectives for different types of network models, such as signed, dynamic, and multidimensional.

Evolutionary Computation for Community Detection in Networks: A Review

Uncovering community structures of a complex network can help us to understand how the network functions. Over the past few decades, network community detection has attracted growing research interest from many fields. Many community detection methods have been developed. Network community structure detection can be modelled as optimisation problems. Due to their inherent complexity, these problems often cannot be well solved by traditional optimisation methods. For this reason, evolutionary algorithms have been adopted as a major tool for dealing with community detection problems. This paper presents a survey on evolutionary algorithms for network community detection. The evolutionary algorithms in this survey cover both single objective and multiobjective optimisations. The network models involve weighted/unweighted, signed/unsigned, overlapping/non-overlapping and static/dynamic ones.

A survey on network community detection based on evolutionary computation

Detecting communities from complex networks has triggered considerable attention in several application domains. Targeting this problem, a local search based genetic algorithm (GALS) which employs a graph-based representation (LAR) has been proposed in this work. The core of the GALS is a local search based mutation technique. Aiming to overcome the drawbacks of the existing mutation methods, a concept called marginal gene has been proposed, and then an effective and efficient mutation method, combined with a local search strategy which is based on the concept of marginal gene, has also been proposed by analyzing the modularity function. Moreover, in this paper the percolation theory on ER random graphs is employed to further clarify the effectiveness of LAR presentation; A Markov random walk based method is adopted to produce an accurate and diverse initial population; the solution space of GALS will be significantly reduced by using a graph based mechanism. The proposed GALS has been tested on both computer-generated and real-world networks, and compared with some competitive community mining algorithms. Experimental result has shown that GALS is highly effective and efficient for discovering community structure.

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Genetic Algorithm with Local Search for Community Mining in Complex Networks

Community detection in complex networks is a topic of considerable recent interest within the scientific community. For dealing with the problem that genetic algorithm are hardly applied to community detection, we propose a genetic algorithm with ensemble learning (GAEL) for detecting community structure in complex networks. GAEL replaces its traditional crossover operator with a multi-individual crossover operator based on ensemble learning. Therefore, GAEL can avoid the problems that are brought by traditional crossover operator which is only able to mix string blocks of different individuals, but not able to recombine clustering contexts of different individuals into new better ones. In addition, the local search strategy, which makes mutated node be placed into the community where most of its neighbors are, is used in mutation operator. At last, a Markov random walk based method is used to initialize population in this paper, and it can provide us a population of accurate and diverse clustering solutions. Those diverse and accurate individuals are suitable for ensemble learning based multi-individual crossover operator. The proposed GAEL is tested on both computer-generated and real-world networks, and compared with current representative algorithms for community detection in complex networks. Experimental results demonstrate that GAEL is highly effective at discovering community structure.

Chunguang Zhou

Papers

Genetic Algorithm with Ensemble Learning for Detecting Community Structure in Complex Networks

Genetic Algorithm with Ensemble Learning for Detecting Community Structure in Complex Networks