Community Detection In Complex Networks Research Articles

Complex Network Community Detection based on Genetic Algorithm using K-cliques

Inspired by the genetic algorithm, this paper proposes a complex network community detection algorithm, which searches for complete sub-graphs in a network, the numbers of nodes are greater than or equal to k, also referred to maximum k-cliques. K-cliques are the most connected communities in a network. Using the maximum k-clique initializes the population, which can improve the accuracy and efficiency of population initialization. K-clique-based population initialization, crossover, mutation, μ+λ selection strategy, and evaluation function (Q function) are adopted to select the next generation of population, the superior traits formed by parents during the process of evolution cannot be destroyed and can also be effectively inherited by offspring individuals. Finally, community partition is optimized using the fast Newman algorithm. This step performs further clustering on the communities. The algorithm can reduce the search space of community partition and improve the search efficiency of the algorithm. It is tested on benchmark networks and real-world networks. The algorithm has an acceptable time complexity. The experimental results show that the algorithm can effectively divide the communities.

Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks.

Multiobjective Particle Swarm Optimization Based on Network Embedding for Complex Network Community Detection

Community detection in complex networks is significant to social network analysis. Most of the algorithms take advantage of single-objective optimization methods, which may not be effective for complex networks. Compared with single-objective algorithms, multiobjective evolutionary algorithms can avoid local optimization. However, multiobjective evolutionary algorithms often encounter problems of excessive search space and low efficiency. To solve these issues, this study introduces network embedding into the multiobjective particle swarm algorithm and maps nodes into a low-latitude space, thereby effectively reducing the search space while increasing search efficiency via a consensus propagation strategy. Experimental results demonstrate that a novel effective algorithm based on multiobjective particle swarm optimization (NE-PSO) performs effectively and has competitive performance in comparison with state-of-the-art approaches on synthetic and real-world networks, especially the large-scale ones.

A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

Significance-based multi-scale method for network community detection and its application in disease-gene prediction.

Community detection in complex networks is an important issue in network science. Several statistical measures have been proposed and widely applied to detecting the communities in various complex networks. However, due to the lack of flexibility resolution, some of them have to encounter the resolution limit and thus are not compatible with multi-scale structures of complex networks. In this paper, we investigated a statistical measure of interest for community detection, Significance [Sci. Rep. 3 (2013) 2930], and analyzed its critical behaviors based on the theoretical derivation of critical number of communities and the phase diagram in community-partition transition. It was revealed that Significance exhibits far higher resolution than the traditional Modularity when the intra- and inter-link densities of communities are obviously different. Following the critical analysis, we developed a multi-resolution version of Significance for identifying communities in the multi-scale networks. Experimental tests in several typical networks have been performed and confirmed that the generalized Significance can be competent for the multi-scale communities detection. Moreover, it can effectively relax the first- and second-type resolution limits. Finally, we displayed an important potential application of the multi-scale Significance in computational biology: disease-gene identification, showing that extracting information from the perspective of multi-scale module mining is helpful for disease gene prediction.

CoVeC: Coarse-grained vertex clustering for efficient community detection in sparse complex networks

This paper tackles the problem of community detection in large-scale graphs. In the literature devoted to this topic, an iterative algorithm, called Louvain Method (LM), stands out as an effective and fast solution for this problem. However, the first iterations of the LM are the most costly. To overcome this issue, this paper introduces CoVeC, a Coarse-grained Vertex Clustering for efficient community detection in sparse complex networks. CoVeC pre-processes the original graph in order to forward a graph of reduced size to the LM. The subsequent group formation, including the maximization of group quality, as per the modularity metric, is left to the LM. We evaluate our proposal using real-world and synthetic networks, presenting distinct sizes and sparsity levels. Overall, our experimental results show that CoVeC can be a way faster option than the first iterations of the LM, yet similarly effective. In fact, for sparser graphs, the combo CoVeC+LM outperforms the standalone LM and its variations, attaining a mean processing time reduction of 47% and a mean modularity reduction of only 0.4%.

Community detection in complex networks with an ambiguous structure using central node based link prediction

Community detection in complex networks has aroused wide attention, since it can find some useful information hidden in the networks. Many different community detection algorithms have been proposed to detect the communities in a variety of networks. However, as the ratio of each node connecting with the nodes in other communities increases, namely, the community structure of networks becomes unclear, the performance of most existing community detection algorithms will considerately deteriorate. As a method of finding missing information, link prediction can predict undiscovered edges in the networks. However, the existing link prediction based community detection algorithms cannot deal with the networks with an ambiguous community structure, namely, the networks having a mixing parameter greater than 0.5. In this paper, we design a new strategy of link prediction and propose a community detection algorithm based on this strategy to detect the communities in complex networks, especially for the networks with an ambiguous community structure. Experimental results on synthetic benchmark networks and real-world networks indicate that the proposed community detection algorithm outperforms five state-of-the-art community detection algorithms, especially for those without a clear community structure.

Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends

Over the past couple of decades, the research area of network community detection has seen substantial growth in popularity, leading to a wide range of researches in the literature. Nature-inspired optimization algorithms (NIAs) have given a significant contribution to solving the community detection problem by transcending the limitations of other techniques. However, due to the importance of the topic and its prominence in many applications, the information on it is scattered in various journals, conference proceedings, and patents, and lacked a focused-literature that synthesizes them in a single document. This review aims to provide an overview of the NIAs and their role in solving community detection problems. To achieve this goal, a systematic study is performed on NIAs, followed by historical and statistical analysis of the researches involved. This would lead to the identification of future trends, as well as the discovery of related research challenges. This review provides a guide for researchers to identify new areas of research, as well as directing their future interest towards developing more effective frameworks in the context of nature-inspired community detection algorithms.

An iterated local search algorithm for community detection in complex networks

Community detection is one of the most challenging problems in complex network analysis. This problem attracts an amount of interest from various scientific fields such as biology, social network and physics. In the past few decades, numerous heuristics and exact algorithms have been designed to address the problem. However, most of them are not suitable for large networks, since they require considerable computing time. Contrary to the recent trend in the community detection literature, where complex nature-inspired methods are often proposed, we present a simple metaheuristic approach based on the Iterated Local Search (ILS) algorithm which has been applied with great success to the related problems. Extensive comparative evaluations are carried out against the state-of-the-art techniques for the problem in the literature. The computational results show that ILS can detect communities with high quality and stability.

Multiplex community detection in complex networks using an evolutionary approach

Multiplex networks are the general representative of complex systems composed of distinct interactions between the same entities on multiple layers. Community detection in the multiplex networks is the problem of finding a shared structure under all layers, which combines the information of the entire network. Most of the existing methods for community detection in the single-layer networks cannot be well applied to detect shared communities in multiplex networks. In this paper, we employ a multi-objective evolutionary approach, namely Multi-Objective Evolutionary Algorithm based on Decomposition with Tabu Search (MOEA/D-TS), to detect shared communities in multiplex networks. Also, we have improved the MOEA/D-TS using a social networks analysis measure named Clustering Coefficient (CC) in terms of the generation of the initial population. This hybrid algorithm employs the parallel computing capacity of the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) along with the neighborhood search authority of Tabu Search (TS) for discovering Pareto optimal solutions. Extensive experiments on a variety of single-layer and multiplex real-world data sets show the superiority of the proposed method in comparison to state-of-the-art algorithms and its capability for producing improved results.

Community Detection in Complex Networks Using Nonnegative Matrix Factorization and Density-Based Clustering Algorithm

Community detection is a critical issue in the field of complex networks. Capable of extracting inherent patterns and structures in high dimensional data, the non-negative matrix factorization (NMF) method has become one of the hottest research topics in community detection recently. However, this method has a significant drawback; most community detection methods using NMF require the number of communities to be preassigned or determined by searching for the best community structure among all candidates. To address the problem, in this paper, we use an improved density peak clustering to obtain the number of cores as the pre-defined parameter of nonnegative matrix factorization. Then we adopt nonnegative double singular value decomposition initialization which can rapidly reduce the approximation error of nonnegative matrix factorization. Finally, we compare and analyze the performance of different algorithms on artificial networks and real-world networks. Experimental results indicate that the proposed method is superior to the state-of-the-art methods.

A variant of EAM to uncover community structure in complex networks

Environmental adaptation method (EAM) was developed to solve single-objective optimisation problems. After the first proposal, other variants have been suggested to speed up the convergence rate and to maintain the diversity of the solutions. Among those variants, IEAM-RP works with real numbers. In this paper, a variant of IEAM-RP has been suggested with major changes in adaptation operator to improve the overall performance of the algorithm. In the proposed method, significant attention has been given for balancing exploration and exploitation of individuals in the population. The performance of the proposed algorithm is compared against 14 state-of-the-art algorithms using standard benchmark functions of the comparing continuous optimisers (COCO) framework. Further, to check the effectiveness of the proposed approach, it has been applied to a real-world problem of community detection in complex networks. Again, the experimental results are found very promising and competitive compared to other algorithms.

Local Optimization for Clique-Based Overlapping Community Detection in Complex Networks

Detecting communities in complex networks has been one of the most popular research areas in recent years. There have been many community detection algorithms proposed to date. However, the local information (cliques) of communities and the search efficiency of algorithm have not been considered both in previous studies. In this paper, we propose a novel local expansion algorithm for detecting overlapping communities based on cliques. The algorithm draws on the assumption that cliques are the core of communities, as the clique takes into account the local characteristics of the community. The proposed algorithm adopts a single node with the maximum density as an initial community to prevent the formation of a large number of near-duplicate community structures, which improves the search efficiency of the algorithm. In many experiments using computer-generated and real-world networks, the proposed algorithm based on this idea verifies that the algorithm is able to detect overlapping communities effectively. The experiment yields better community uncover results, and the time efficiency and the complexity of algorithm are also satisfactory.

The Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

As an important tool of social network analysis, network representation learning also called network embedding maps the network to a latent space and learns low-dimensional and dense real vectors of nodes, while preserving the structure and internal attributes of network. The learned representations or embedding vectors can be used for node clustering, link prediction, network visualization and other tasks for network analysis. Most of the existing network representation learning algorithms mainly focus on the preservation of micro or macro network structure, ignoring the mesoscopic community structure information. Although a few network embedding methods are proposed to preserve the community structure, they all ignore the prior information about communities. Inspired by the semi-supervised community detection in complex networks, in this article, a novel Semi-Supervised DeepWalk method(SSDW) is proposed for network representation learning, which successfully preserves the community structure of network in the embedding space. Specifically, a semi-supervised random walk sampling method which effectively integrates the pairwise constraints is proposed. By doing so, the SSDW model can guide the transition probability in the random walk process and obtain the node context sequence in line with the prior knowledge. The experimental results on eight real networks show that comparing with the popular network embedding methods, the node representation vectors integrating pairwise constraints into the random walk process can obtain higher accuracy on node clustering task, and the results of link prediction, network visualization tasks indicate that the semi-supervised model SSDW is more discriminative than unsupervised ones.

A Decomposition-Based Multiobjective Chemical Reaction Optimization Algorithm for Community Detection in Complex Networks

Community detection structure is very important for understanding the organization of the complex networks. This problem is NP-hard, which is modeled as a seriously nonlinear optimization problem. Recently, different intelligence algorithm has shown promising results for this problem. The chemical reaction optimization (CRO) algorithm is a novel evolutionary algorithm which mimics the phenomenon of interactions among molecules in a container. The one characteristic of CRO is that the size of the population is changing. In this paper, we redefined the operator of CRO, and using the method of multiobjective decomposition decomposed the community detection problem into a scalar of sub-problems and using the proposed a discrete variant of CRO (MODCRO) to optimization. In the proposed method, neighbor-based turbulence of on-wall ineffective collision operator and decomposition operator are redefined which is responsible for searching local exploitation ability of algorithm, and the intermolecular ineffective collisions operator and synthesis operator is also redesigned which is responsible for searching global exploration ability of algorithm. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art multiobjective optimization evolutionary algorithms (MOEAs) on modularity.

Community detection in complex networks using multi-objective bat algorithm

Community detection is the problem of identifying communities in which we aim to discover groups of nodes with high connectivity within the same group and with low connectivity outside the group. Community detection is considered to be a non-deterministic polynomial-time hard problem. Heuristic algorithms can be used to solve such a complex optimisation problem. Bat algorithm (BA) is a meta-heuristic optimisation algorithm. The BA can be used to model a multi-objective optimisation problem. In this paper, the multi-objective bat algorithm (MOBA) is adapted to model and solve the community detection problem. In order to evaluate the algorithm, four real-world datasets are used. The performance of the algorithm is compared with seven other methods from the literature. The comparison was in terms of two metrics to check the quality of the obtained community namely modularity (Q) and normalised mutual information (NMI). The results show that the proposed algorithm outperforms all algorithms in one dataset and that it is competitive in other cases.

Community Detection Algorithm Based on Nonnegative Matrix Factorization and Improved Density Peak Clustering

Community detection is a critical issue in the field of complex networks. Recently, the nonnegative matrix factorization (NMF) method has successfully uncovered the community structure in the complex networks. However, this method has a significant drawback; most of community detection methods using NMF require the number of communities to be preassigned or determined the number of communities by searching for the best community structure among all candidates. To address this problem, in this paper, we use density peak clustering (DPC) to obtain the number of centers as the pre-defined parameter for nonnegative matrix factorization. However, due to sparse and high dimensional characteristics of complex networks, DPC cannot be used to detect community directly. To overcome this issue, we employ degree and hop of nodes as the density and distance indexes, respectively; we use NMF and Symmetric NMF to deal with linearly separable data and non-linearly separable data, respectively. Experimental results show that the proposed methods exhibit excellent performance on artificial and real-world networks and superior to the state-of-the-art methods which are the most common method for community detection of complex networks.

Community detection in complex networks using multi-objective bat algorithm

Community detection is the problem of identifying communities in which we aim to discover groups of nodes with high connectivity within the same group and with low connectivity outside the group. Community detection is considered to be a non-deterministic polynomial-time hard problem. Heuristic algorithms can be used to solve such a complex optimisation problem. Bat algorithm (BA) is a meta-heuristic optimisation algorithm. The BA can be used to model a multi-objective optimisation problem. In this paper, the multi-objective bat algorithm (MOBA) is adapted to model and solve the community detection problem. In order to evaluate the algorithm, four real-world datasets are used. The performance of the algorithm is compared with seven other methods from the literature. The comparison was in terms of two metrics to check the quality of the obtained community namely modularity (Q) and normalised mutual information (NMI). The results show that the proposed algorithm outperforms all algorithms in one dataset and that it is competitive in other cases.

Unsupervised graph clustering for community detection in complex networks using spectral analysis

Unsupervised graph clustering for community detection in complex networks using spectral analysis

Unsupervised Graph Clustering for community Detection in Complex Networks using Spectral Analysis

Clustering is a recent technique for a smart classification of data, where the output is a set of clusters and each cluster regroups data points having similar behaviours. Traditional clustering algorithms are those where we predefine the number of clusters as an input parameter, and we control the size of the neighbourhood, also called supervised clustering. Recently, with data evolution in term of volume and variety, supervised clustering techniques were overwhelmed and unsupervised algorithms started to appear. Spectral clustering (SC) is a graph clustering technique based on spectral analysis, and it is one of the most powerful unsupervised clustering techniques. This paper presents an application of a spectral clustering algorithm to data modelled by graphs and a comparison between the two families of SC; unnormalised SC and normalised SC. We also introduce a modification of normalised SC algorithm to make the number of clusters estimated and not given as an input parameter. Further works are needed to apply the approach to larger datasets in order to evaluate its performance against big data challenges.