A Novel Genetic Algorithm for Overlapping Community Detection

Cai, Yanan; Shi, Chuan; Dong, Yuxiao; Ke, Qing; Wu, Bin

doi:10.1007/978-3-642-25853-4_8

Cited by 19 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key technique lies in the evaluation of an overlapped community. As long as an evaluation criterion is decided, bio-inspired metaheuristics can be easily utilised to solve this problem (Pizzuti, 2009b;Liu et al, 2010;Cai et al, 2011;Lin et al, 2013c). For more information about the fitness evaluation for overlapping communities, please refer to Chira and Gog (2011) and Xie et al (2011).…”

Section: Single Objective Optimisation 411 Modularity-based Modelmentioning

confidence: 99%

A survey on network community detection based on evolutionary computation

Cai

Gong

et al. 2016

IJBIC

109

View full text Add to dashboard Cite

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. . His current research interests are in the area of computational intelligence and complex network analysis. Lijia Ma received his BS in Communication Engineering from His research interests include evolutionary multiobjective optimisation, data mining and complex network analysis. Maoguo Gong received his BS in Electronic Engineering and PhD in Electronic Science and Technology from Xidian University, Xi'an, China, in 2003 and 2009, respectively. He is currently a Full Professor with the Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, Xidian University. His research interests are broadly in the area of computational intelligence with applications. He has published over 100 research papers in journals and conferences, and holds 12 granted patents as the first inventor.A survey on network community detection based on evolutionary computation 85 Dayong Tian received his BS of Electronic Information Science and Techonology and ME of Circuit and System in Xidian University, Xi'an, China. Currently, he is a PhD student in Biomedical Engneering in University of Technology, Sydney, Australia. His research interests are machine learning theory and applications in face image restoration, recognition and image retrieval. We expect that complex network analysis's scope will continue to expand and its applications to multiply. We are positive that methods and theories that work for community detection are helpful for other network issues such as link prediction, network recommender, network robustness, network resource allocation, anomaly detection, network core detection, network ranking, network compression, network security, network sentiment computing, and so forth. From both theoretical and technological perspectives, network community detection technology will move beyond network structure analysis toward emphasising network intelligence. We do hope that this survey can benefit scholars who set foot in this field. Our future work will focus on more in-depth analysis of network issues. Such analysis is expected to shed light on how networks change the real world.

show abstract

Section: Single Objective Optimisation 411 Modularity-based Modelmentioning

confidence: 99%

A survey on network community detection based on evolutionary computation

Cai

Gong

et al. 2016

IJBIC

109

View full text Add to dashboard Cite

show abstract

“…With proper threshold our algorithm divides it into two communities with the same result as algorithms mentioned before. Our algorithm detects overlapping vertices by testing vertex4, 5, 7 with a good judgment [35] (details will be discussed in Section3.3). Vertex7 is detected as a overlapping vertex by our algorithm in this network.…”

Section: Motivating Examplementioning

confidence: 99%

“…We think vertexCvertex connected densely in a community is key feature of community structure. So we adopt average degree of the community [35] which is based on the concept of partition density to evaluate a vertex is a overlapping vertex or not. The definition of average degree is…”

Section: Overlapping Vertices Detectionmentioning

confidence: 99%

LED: A fast overlapping communities detection algorithm based on structural clustering

Wang

Tang

et al. 2016

Neurocomputing

View full text Add to dashboard Cite

Community detection in social networks is a fundamental task of complex network analysis. Community is usually regarded as a functional unit. Networks in real world more or less have overlapping community structure while traditional community detection algorithms assume that one vertex can only belong to one community. This paper proposes an efficient overlapping community detection algorithm named LED (Loop Edges Delete). LED algorithm is based on Structural Clustering, which converts structural similarity between vertices to weights of network. The evaluations of the LED algorithm are conducted both from classical networks from literature and C-DBLP, which is a huge and real-life co-author social network in China. The results show LED is superior to other methods in accuracy, efficiency, comparing with FastModurity and GN algorithm.

show abstract

“…The key technique lies in the evaluation of an overlapped community. As long as an evaluation criterion is decided, bio-inspired metaheuristics can be easily utilized to solve this problem [104,84,24,81]. For more information about the fitness evaluation for overlapping communities, please refer to [28,129].…”

Section: Existing Approaches For Community Discoverymentioning

confidence: 99%

Big Network Analytics Based on Nonconvex Optimization

Gong

Cai

et al. 2016

Studies in Big Data

View full text Add to dashboard Cite

The scientific problems that Big Data faces may be network scientific problems. Network analytics contributes a great deal to networked Big Data processing. Many network issues can be modeled as nonconvex optimization problems and consequently they can be addressed by optimization techniques. In the pipeline of nonconvex optimization techniques, evolutionary computation gives an outlet to handle these problems efficiently. Because, network community discovery is a critical research agenda of network analytics, in this chapter we focus on the evolutionary computation based nonconvex optimization for network community discovery. The single and multiple objective optimization models for the community discovery problem are thoroughly investigated. Several experimental studies are shown to demonstrate the effectiveness of optimization based approach for big network community analytics.

show abstract

A Novel Genetic Algorithm for Overlapping Community Detection

Cited by 19 publications

References 11 publications

A survey on network community detection based on evolutionary computation

A survey on network community detection based on evolutionary computation

LED: A fast overlapping communities detection algorithm based on structural clustering

Big Network Analytics Based on Nonconvex Optimization

Contact Info

Product

Resources

About