Research on Community Detection in Complex Networks Based on Internode Attraction

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.

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

Applied Sciences ◽

10.3390/app10093126 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3126

Author(s):

Desheng Lyu ◽

Bei Wang ◽

Weizhe Zhang

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Community Structure ◽

Local Search ◽

Complex Networks ◽

Community Detection ◽

Large Scale ◽

Particle Swarm ◽

Search Performance ◽

Swarm Optimization

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.

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Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

Mathematical Problems in Engineering ◽

10.1155/2014/502809 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Vinícius da Fonseca Vieira ◽

Carolina Ribeiro Xavier ◽

Nelson Francisco Favilla Ebecken ◽

Alexandre Gonçalves Evsukoff

Keyword(s):

Spectral Method ◽

Community Detection ◽

High Performance ◽

Large Scale ◽

Fine Tuning ◽

Detection Algorithms ◽

Research Areas ◽

Structure Detection ◽

Wide Range ◽

Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

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A Local Overlapping Community Detection Method in Complex Networks

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.985 ◽

2012 ◽

Vol 6-7 ◽

pp. 985-990

Author(s):

Yan Peng ◽

Yan Min Li ◽

Lan Huang ◽

Long Ju Wu ◽

Gui Shen Wang ◽

...

Keyword(s):

Community Structure ◽

Complex Networks ◽

Community Detection ◽

Real World ◽

Detection Method ◽

Experimental Results ◽

Overlapping Community Detection ◽

Greedy Strategy ◽

Structure Detection ◽

Overlapping Community

Community structure detection has great importance in finding the relationships of elements in complex networks. This paper presents a method of simultaneously taking into account the weak community structure definition and community subgraph density, based on the greedy strategy for community expansion. The results are compared with several previous methods on artificial networks and real world networks. And experimental results verify the feasibility and effectiveness of our approach.

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An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Symmetry ◽

10.3390/sym13050869 ◽

2021 ◽

Vol 13 (5) ◽

pp. 869

Author(s):

Mingqing Huang ◽

Qingshan Jiang ◽

Qiang Qu ◽

Abdur Rasool

Keyword(s):

Community Detection ◽

Matrix Factorization ◽

Large Scale ◽

Nonnegative Matrix Factorization ◽

Nonnegative Matrix ◽

Overlapping Community Detection ◽

Detection Approach ◽

Overlapping Community ◽

Large Scale Networks ◽

Symmetric Nonnegative Matrix Factorization

Overlapping clustering is a fundamental and widely studied subject that identifies all densely connected groups of vertices and separates them from other vertices in complex networks. However, most conventional algorithms extract modules directly from the whole large-scale graph using various heuristics, resulting in either high time consumption or low accuracy. To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF). It primarily divides the whole network into many sub-graphs under the premise of preserving the clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network. Experiments on both synthetic and real-world networks of publicly available datasets demonstrate that the proposed approach outperforms the state-of-the-art methods for community detection in large-scale networks.

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A UNIFIED COMMUNITY DETECTION ALGORITHM IN LARGE-SCALE COMPLEX NETWORKS

Advances in Complex Systems ◽

10.1142/s0219525919500048 ◽

2019 ◽

Vol 22 (03) ◽

pp. 1950004

Author(s):

HAO LONG ◽

XIAO-WEI LIU

Keyword(s):

Complex Networks ◽

Community Detection ◽

Large Scale ◽

Detection Algorithm ◽

Component Structure ◽

Overlapping Communities ◽

Expansion Strategy ◽

High Scalability ◽

Community Detection Algorithm ◽

Large Scale Networks

A community is the basic component structure of complex networks and is important for network analysis. In recent decades, researchers from different fields have witnessed a boom of community detection, and many algorithms were proposed to retrieve disjoint or overlapping communities. In this paper, a unified expansion approach is proposed to obtain two different network partitions, which can provide divisions with higher accuracies and have high scalability in large-scale networks. First, we define the edge intensity to quantify the densities of network edges, a higher edge intensity indicates a more compact pair of nodes. Second, vertices of higher density edges are extracted out and denoted as core nodes, whereas other vertices are treated as margin nodes; finally we apply an expansion strategy to form disjoint communities: closely connected core nodes are combined as disjoint skeleton communities, and margin nodes are gradually attached to the nearest skeleton communities. To detect overlapping communities, extra steps are adopted: potential overlapping nodes are identified from the existing disjoint communities and replicated; and communities that bear replicas are further partitioned into smaller clusters. Because replicas of potential overlapping nodes might remain in different communities, overlapping communities can be acquired. Experimental results on real and synthetic networks illustrate higher accuracy and better performance of our method.

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Community Detection Based on DeepWalk Model in Large-Scale Networks

Security and Communication Networks ◽

10.1155/2020/8845942 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Yunfang Chen ◽

Li Wang ◽

Dehao Qi ◽

Tinghuai Ma ◽

Wei Zhang

Keyword(s):

Community Structure ◽

Community Detection ◽

Large Scale ◽

Dimensional Space ◽

Complex Structure ◽

Gaussian Mixture ◽

Detection Algorithm ◽

Detection Methods ◽

Large Scale Networks ◽

Low Dimensional

The large-scale and complex structure of real networks brings enormous challenges to traditional community detection methods. In order to detect community structure in large-scale networks more accurately and efficiently, we propose a community detection algorithm based on the network embedding representation method. Firstly, in order to solve the scarce problem of network data, this paper uses the DeepWalk model to embed a high-dimensional network into low-dimensional space with topology information. Then, low-dimensional data are processed, with each node treated as a sample and each dimension of the node as a feature. Finally, samples are fed into a Gaussian mixture model (GMM), and in order to automatically learn the number of communities, variational inference is introduced into GMM. Experimental results on the DBLP dataset show that the model method of this paper can more effectively discover the communities in large-scale networks. By further analyzing the excavated community structure, the organizational characteristics within the community are better revealed.

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AGGLOMERATIVE CLUSTERING BASED ON LABEL PROPAGATION FOR DETECTING OVERLAPPING AND HIERARCHICAL COMMUNITIES IN COMPLEX NETWORKS

Advances in Complex Systems ◽

10.1142/s0219525914500210 ◽

2014 ◽

Vol 17 (06) ◽

pp. 1450021 ◽

Cited By ~ 3

Author(s):

YUXIN ZHAO ◽

SHENGHONG LI ◽

SHILIN WANG

Keyword(s):

Community Structure ◽

Complex Networks ◽

Large Scale ◽

Label Propagation ◽

Agglomerative Clustering ◽

Overlapping Communities ◽

Small Communities ◽

Structural And Functional Properties ◽

Overlapping Community ◽

Large Scale Networks

Community detection is an important issue to understand the structural and functional properties of complex networks, which still remains a challenging subject. In some complex networks, a node may belong to multiple communities, implying overlapping community structure. Moreover, complex networks often show a hierarchical structure where small communities group together to form larger ones. In this paper, we propose a novel parameter-free algorithm called agglomerative clustering based on label propagation algorithm (ACLPA) to detect both overlapping and hierarchical community structure in complex networks. By combining the advantages of agglomerative clustering and label propagation, our algorithm can build the hierarchical tree of overlapping communities in large-scale networks. The tests on both synthetic and real-world networks give excellent results and demonstrate the effectiveness and efficiency of our algorithm.

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A multi-objective ant colony optimization with decomposition for community detection in complex networks

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218804002 ◽

2019 ◽

Vol 41 (9) ◽

pp. 2521-2534 ◽

Cited By ~ 1

Author(s):

Ruochen Liu ◽

Jiangdi Liu ◽

Manman He

Keyword(s):

Complex Networks ◽

Ant Colony Optimization ◽

Community Detection ◽

Large Scale ◽

Ant Colony Algorithm ◽

Ant Colony ◽

Network Clustering ◽

Multi Objective ◽

Search Mechanism ◽

Large Scale Networks

Community detection in complex networks plays an important role in mining and analyzing the structure and function of networks. However, traditional algorithms for community detection-based graph partition and hierarchical clustering usually have to face expensive computational costs or require some specific conditions when dealing with complex networks. Recently, community detection based on intelligent optimization attracts more and more attention because of its good effectiveness. In this paper, a new multi-objective ant colony optimization with decomposition (MACOD) for community detection in complex networks is proposed. Firstly, a new framework of multi-objective ant colony algorithm specialized initially for the complex network clustering is developed, in which two-objective optimization problem can be decomposed into a series of subproblems and each ant is responsible for one single objective subproblem and it targets a particular point in the Pareto front. Secondly, a problem-specific individual encoding strategy based on graph is proposed. Moreover, a new efficient local search mechanism is designed in order to improve the stability of the algorithm. The proposed MACOD has been compared with four other state of the art algorithms on two benchmark networks and seven real-world networks including three large-scale networks. Experimental results show that MACOD performs competitively for the community detection problems.

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Modelling community structure and temporal spreading on complex networks

Computational Social Networks ◽

10.1186/s40649-021-00094-z ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Vesa Kuikka

Keyword(s):

Community Structure ◽

Complex Networks ◽

Community Detection ◽

Network Structure ◽

Network Connectivity ◽

Network Models ◽

Building Blocks ◽

Detection Algorithm ◽

Research Area ◽

Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

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Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201342 ◽

2021 ◽

pp. 1-17

Author(s):

Mohammed Al-Andoli ◽

Wooi Ping Cheah ◽

Shing Chiang Tan

Keyword(s):

Particle Swarm Optimization ◽

Complex Networks ◽

Learning Community ◽

Community Detection ◽

Particle Swarm ◽

Premature Convergence ◽

Swarm Optimization ◽

Detection Algorithms ◽

Real World Datasets ◽

The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

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