scholarly journals Community Detection Based on Differential Evolution Using Modularity Density

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 218 ◽  
Author(s):  
Caihong Liu ◽  
Qiang Liu
Keyword(s):  
Community Structure ◽  
Differential Evolution ◽  
Community Detection ◽  
Differential Evolution Algorithm ◽  
Limit Problem ◽  
Detection Methods ◽  
Tuning Parameter ◽  
Resolution Limit ◽  
Science Field ◽  
Modularity Density

Currently, many community detection methods are proposed in the network science field. However, most contemporary methods only employ modularity to detect communities, which may not be adequate to represent the real community structure of networks for its resolution limit problem. In order to resolve this problem, we put forward a new community detection approach based on a differential evolution algorithm (CDDEA), taking into account modularity density as an optimized function. In the CDDEA, a new tuning parameter is used to recognize different communities. The experimental results on synthetic and real-world networks show that the proposed algorithm provides an effective method in discovering community structure in complex networks.

Density-based Approach with Dual Optimization for Tracking Community Structure of Increasing Social Networks

2020 ◽  
Vol 29 (01) ◽  
pp. 2050002
Author(s):  
Fariza Bouhatem ◽  
Ali Ait El Hadj ◽  
Fatiha Souam
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Rapid Detection ◽  
Dynamic Networks ◽  
Rapid Evolution ◽  
Limit Problem ◽  
Resolution Limit ◽  
Normalized Mutual Information ◽  
Information Number ◽  
Modularity Density

The rapid evolution of social networks in recent years has focused the attention of researchers to find adequate solutions for the management of these networks. For this purpose, several efficient algorithms dedicated to the tracking and the rapid detection of the community structure have been proposed. In this paper, we propose a novel density-based approach with dual optimization for tracking community structure of increasing social networks. These networks are part of dynamic networks evolving by adding nodes with their links. The local optimization of the density makes it possible to reduce the resolution limit problem generated by the optimization of the modularity. The presented algorithm is incremental with a relatively low algorithmic complexity, making it efficient and faster. To demonstrate the effectiveness of our method, we test it on social networks of the real world. The experimental results show the performance and efficiency of our algorithm measured in terms of modularity density, modularity, normalized mutual information, number of communities discovered, running time and stability of communities.

scholarly journals LIMITED RESOLUTION AND MULTIRESOLUTION METHODS IN COMPLEX NETWORK COMMUNITY DETECTION

2007 ◽  
Vol 07 (03) ◽  
pp. L209-L214 ◽  
Author(s):  
JUSSI M. KUMPULA ◽  
JARI SARAMÄKI ◽  
KIMMO KASKI ◽  
JÁNOS KERTÉSZ
Keyword(s):  
Simulated Annealing ◽  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Tuning Parameter ◽  
Challenging Problem ◽  
Resolution Limit ◽  
Network Community ◽  
Multiresolution Methods

Detecting community structure in real-world networks is a challenging problem. Recently, it has been shown that the resolution of methods based on optimizing a modularity measure or a corresponding energy is limited; communities with sizes below some threshold remain unresolved. One possibility to go around this problem is to vary the threshold by using a tuning parameter, and investigate the community structure at variable resolutions. Here, we analyze the resolution limit and multiresolution behavior for two different methods: a q-state Potts method proposed by Reichard and Bornholdt, and a recent multiresolution method by Arenas, Fernández, and Gómez. These methods are studied analytically, and applied to three test networks using simulated annealing.

scholarly journals MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 49
Author(s):  
Junliang Shang ◽  
Yiting Li ◽  
Yan Sun ◽  
Feng Li ◽  
Yuanyuan Zhang ◽  
...  
Keyword(s):  
Community Detection ◽  
System Analysis ◽  
Optimal Solution ◽  
Limit Problem ◽  
Detection Methods ◽  
Selection Strategy ◽  
Adjusted Rand Index ◽  
Resolution Limit ◽  
Normalized Mutual Information ◽  
Multi Objective

Community detection is a hot research direction of network science, which is of great importance to complex system analysis. Therefore, many community detection methods have been developed. Among them, evolutionary computation based ones with a single-objective function are promising in either benchmark or real data sets. However, they also encounter resolution limit problem in several scenarios. In this paper, a Multi-Objective Pigeon-Inspired Optimization (MOPIO) method is proposed for community detection with Negative Ratio Association (NRA) and Ratio Cut (RC) as its objective functions. In MOPIO, the genetic operator is used to redefine the representation and updating of pigeons. In each iteration, NRA and RC are calculated for each pigeon, and Pareto sorting scheme is utilized to judge non-dominated solutions for later crossover. A crossover strategy based on global and personal bests is designed, in which a compensation coefficient is developed to stably complete the work transition between the map and compass operator, and the landmark operator. When termination criteria were met, a leader selection strategy is employed to determine the final result from the optimal solution set. Comparison experiments of MOPIO, with MOPSO, MOGA-Net, Meme-Net and FN, are performed on real-world networks, and results indicate that MOPIO has better performance in terms of Normalized Mutual information and Adjusted Rand Index.

scholarly journals Modelling community structure and temporal spreading on complex networks

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.

Uncovering local community structure on line graph through degree centrality and expansion

2021 ◽  
pp. 2150120
Author(s):  
Guishen Wang ◽  
Kaitai Wang ◽  
Hongmei Wang ◽  
Huimin Lu ◽  
Xiaotang Zhou ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Local Community ◽  
Line Graph ◽  
Detection Methods ◽  
Degree Centrality ◽  
Original Graph ◽  
Normalized Mutual Information ◽  
Local Community Detection ◽  
On Line

Local community detection algorithms are an important type of overlapping community detection methods. Local community detection methods identify local community structure through searching seeds and expansion process. In this paper, we propose a novel local community detection method on line graph through degree centrality and expansion (LCDDCE). We firstly employ line graph model to transfer edges into nodes of a new graph. Secondly, we evaluate edges relationship through a novel node similarity method on line graph. Thirdly, we introduce local community detection framework to identify local node community structure of line graph, combined with degree centrality and PageRank algorithm. Finally, we transfer them back into original graph. The experimental results on three classical benchmarks show that our LCDDCE method achieves a higher performance on normalized mutual information metric with other typical methods.

scholarly journals Variational Approach for Learning Community Structures

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jun Jin Choong ◽  
Xin Liu ◽  
Tsuyoshi Murata
Keyword(s):  
Deep Learning ◽  
Community Structure ◽  
Learning Community ◽  
Community Detection ◽  
Representation Learning ◽  
Semisupervised Learning ◽  
Detection Methods ◽  
Community Structures ◽  
Learning Tasks ◽  
Proposed Model

Discovering and modeling community structure exist to be a fundamentally challenging task. In domains such as biology, chemistry, and physics, researchers often rely on community detection algorithms to uncover community structures from complex systems yet no unified definition of community structure exists. Furthermore, existing models tend to be oversimplified leading to a neglect of richer information such as nodal features. Coupled with the surge of user generated information on social networks, a demand for newer techniques beyond traditional approaches is inevitable. Deep learning techniques such as network representation learning have shown tremendous promise. More specifically, supervised and semisupervised learning tasks such as link prediction and node classification have achieved remarkable results. However, unsupervised learning tasks such as community detection remain widely unexplored. In this paper, a novel deep generative model for community detection is proposed. Extensive experiments show that the proposed model, empowered with Bayesian deep learning, can provide insights in terms of uncertainty and exploit nonlinearities which result in better performance in comparison to state-of-the-art community detection methods. Additionally, unlike traditional methods, the proposed model is community structure definition agnostic. Leveraging on low-dimensional embeddings of both network topology and feature similarity, it automatically learns the best model configuration for describing similarities in a community.

Detecting communities in networks using a Bayesian nonparametric method

2014 ◽  
Vol 28 (28) ◽  
pp. 1450199
Author(s):  
Shengze Hu ◽  
Zhenwen Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Generative Models ◽  
Detection Methods ◽  
Model Parameters ◽  
Nonparametric Method ◽  
Bayesian Nonparametric ◽  
New Community

In the real world, a large amount of systems can be described by networks where nodes represent entities and edges the interconnections between them. Community structure in networks is one of the interesting properties revealed in the study of networks. Many methods have been developed to extract communities from networks using the generative models which give the probability of generating networks based on some assumption about the communities. However, many generative models require setting the number of communities in the network. The methods based on such models are lack of practicality, because the number of communities is unknown before determining the communities. In this paper, the Bayesian nonparametric method is used to develop a new community detection method. First, a generative model is built to give the probability of generating the network and its communities. Next, the model parameters and the number of communities are calculated by fitting the model to the actual network. Finally, the communities in the network can be determined using the model parameters. In the experiments, we apply the proposed method to the synthetic and real-world networks, comparing with some other community detection methods. The experimental results show that the proposed method is efficient to detect communities in networks.

scholarly journals Community structure: A comparative evaluation of community detection methods

2020 ◽  
Vol 8 (1) ◽  
pp. 1-41 ◽  
Author(s):  
Vinh Loc Dao ◽  
Cécile Bothorel ◽  
Philippe Lenca
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Comparative Evaluation ◽  
Structural Information ◽  
Computation Time ◽  
Detection Methods ◽  
Community Size ◽  
Different Types ◽  
Complex Landscape ◽  
Optimization Schemes

AbstractDiscovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practitioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive, and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimization schemes as well as a comparison of their partitioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.

Determining Network Communities Based on Modular Density Optimization

2020 ◽  
Vol 13 (2) ◽  
pp. 128-136 ◽  
Author(s):  
Seema Rani ◽  
Monica Mehrotra
Keyword(s):  
Community Detection ◽  
Bat Algorithm ◽  
Limit Problem ◽  
Resolution Limit ◽  
Community Discovery ◽  
Detection Algorithms ◽  
Network Communities ◽  
Optimization Function ◽  
Np Hard Problem ◽  
Real World Datasets

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.

Uncovering the effective interval of resolution parameter across multiple community optimization measures

2017 ◽  
Vol 31 (06) ◽  
pp. 1750041
Author(s):  
Hui-Jia Li ◽  
Qing Cheng ◽  
He-Jin Mao ◽  
Huanian Wang ◽  
Junhua Chen
Keyword(s):  
Ad Hoc ◽  
Hamiltonian Function ◽  
Limit Problem ◽  
Resolution Limit ◽  
Resolution Parameter ◽  
Discrete Convex Analysis ◽  
Modularity Density ◽  
Density Measure ◽  
Discrete Convex ◽  
Primary Focus

The study of community structure is a primary focus of network analysis, which has attracted a large amount of attention. In this paper, we focus on two famous functions, i.e., the Hamiltonian function [Formula: see text] and the modularity density measure [Formula: see text], and intend to uncover the effective thresholds of their corresponding resolution parameter [Formula: see text] without resolution limit problem. Two widely used example networks are employed, including the ring network of lumps as well as the ad hoc network. In these two networks, we use discrete convex analysis to study the interval of resolution parameter of [Formula: see text] and [Formula: see text] that will not cause the misidentification. By comparison, we find that in both examples, for Hamiltonian function [Formula: see text], the larger the value of resolution parameter [Formula: see text], the less resolution limit the network suffers; while for modularity density [Formula: see text], the less resolution limit the network suffers when we decrease the value of [Formula: see text]. Our framework is mathematically strict and efficient and can be applied in a lot of scientific fields.

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