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 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.

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.

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.

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.

A Memetic Particle Swarm Optimization Algorithm for Community Detection in Complex Networks

2016 ◽  
Vol 30 (02) ◽  
pp. 1659003 ◽  
Author(s):  
Cheng Zhang ◽  
Xinhong Hei ◽  
Dongdong Yang ◽  
Lei Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Limit Problem ◽  
Resolution Limit ◽  
Swarm Optimization

In recent years, community detection has become a hot research topic in complex networks. Many of the proposed algorithms are for detecting community based on the modularity Q. However, there is a resolution limit problem in modularity optimization methods. In order to detect the community structure more effectively, a memetic particle swarm optimization algorithm (MPSOA) is proposed to optimize the modularity density by introducing particle swarm optimization-based global search operator and tabu local search operator, which is useful to keep a balance between diversity and convergence. For comparison purposes, two state-of-the-art algorithms, namely, meme-net and fast modularity, are carried on the synthetic networks and other four real-world network problems. The obtained experiment results show that the proposed MPSOA is an efficient heuristic approach for the community detection problems.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Guoqiang Chen ◽  
Yuping Wang ◽  
Jingxuan Wei
Keyword(s):  
Evolutionary Algorithm ◽  
Community Detection ◽  
Dynamic Networks ◽  
Experimental Studies ◽  
Detection Methods ◽  
Genetic Operators ◽  
Normalized Mutual Information ◽  
Multiobjective Evolutionary Algorithm ◽  
Important Research Topic ◽  
Synthetic Datasets

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.

scholarly journals Social network analysis and community detection on spread of COVID-19

2021 ◽  
Vol 16 (1) ◽  
pp. 37-52
Author(s):  
Ashani Nuwanthika Wickramasinghe ◽  
Saman Muthukumarana
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Label Propagation ◽  
Adjusted Rand Index ◽  
Exponential Random Graph Models ◽  
Normalized Mutual Information ◽  
Information Score ◽  
The Impact

This paper explains the epidemic spread using social network analysis, based on data from the first three months of the 2020 COVID-19 outbreak across the world and in Canada. A network is defined and visualization is used to understand the spread of coronavirus among countries and the impact of other countries on the spread of coronavirus in Canada. The degree centrality is used to identify the main influencing countries. Exponential Random Graph Models (ERGM) are used to identify the processes that influence link creation between countries. The community detection is done using Infomap, Label propagation, Spinglass, and Louvain algorithms. Finally, we assess the community detection performance of the algorithms using adjusted rand index and normalized mutual information score.

scholarly journals A Modularity Degree Based Heuristic Community Detection Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dongming Chen ◽  
Dongqi Wang ◽  
Fangzhao Xia
Keyword(s):  
Computational Complexity ◽  
Community Detection ◽  
Basic Problem ◽  
Optimization Methods ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Resolution Limit ◽  
Measure Function ◽  
Optimization Function ◽  
Community Detection Algorithm

A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm) based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.

An effective community detection method based on one-dimensional “attraction” in network science

2020 ◽  
Vol 31 (05) ◽  
pp. 2050071
Author(s):  
Yi-Yang Yu ◽  
Chuan-Yun Xu ◽  
Ke-Fei Cao
Keyword(s):  
Community Detection ◽  
Network Science ◽  
Linear Time ◽  
Network Size ◽  
Detection Methods ◽  
Large Networks ◽  
One Dimensional ◽  
Normalized Mutual Information ◽  
Effective Community ◽  
The One

Community detection has always been one of the most important issues in network science. With the arrival of the era of big data, it is necessary to develop new accurate and fast community detection methods for the study of many real complex networks (especially large networks). Based on the concept of strong community and the analogy between the edge and the attraction, this paper proposes an effective one-dimensional “attraction” (1DA) method for community detection. The 1DA method uses the number of edges as the measure of the “attraction”. The specific 1DA algorithm is also presented using two effective ways of vertex moving (i.e. the nearest moving and the median moving). After being randomly initialized at different positions on the (one-dimensional) number axis, all vertices will move under the action of the “attraction”; eventually, the vertices of the same community will naturally gather at the same position, while the vertices of different communities will gather at different positions, thus realizing the community division naturally. This method is tested in five typical real networks and one popular benchmark, and compared with several other popular community detection methods. Theoretical analysis and numerical experiments show that the 1DA method can accurately estimate the number of communities, with low (almost linear) time complexity ([Formula: see text], where [Formula: see text] is the network size) and good performance in modularity and normalized mutual information in various networks (especially in the tests in large networks, the 1DA method has the best performance). The 1DA method in this paper provides a simple and practical solution to the problem of community detection.

scholarly journals Community detection based on similarities of communication behavior in IP networks

2020 ◽  
Author(s):  
Shuzhuang Zhang ◽  
Yaning Zhang ◽  
Min Zhou ◽  
Lizhi Peng
Keyword(s):  
Community Detection ◽  
Real World ◽  
Large Scale ◽  
Ip Networks ◽  
Detection Methods ◽  
Communication Behavior ◽  
Normalized Mutual Information ◽  
Public Network ◽  
Ip Network ◽  
Art Community

AbstractCommunities are an important feature of real-world networks that can reveal the structure and dynamic characteristics of networks. Accordingly, the accurate detection and analysis of the community structure in large-scale IP networks is highly beneficial for their optimization and security management. This paper addresses this issue by proposing a novel community detection method based on the similarity of communication behavior between IP nodes, which is determined by analyzing the communication relationships and frequency of interactions between the nodes in the network. On this basis, the nodes are iteratively added to the community with the highest similarity to form the final community division result. The results of experiments involving both complex public network datasets and real-world IP network datasets demonstrate that the proposed method provides superior community detection performance compared to that of four existing state-of-the-art community detection methods in terms of modularity and normalized mutual information indicators.

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