An Analysis of Overlapping Community Detection Algorithms in Social Networks

Clustering Problem ◽

Detection Algorithms ◽

Community Structure is one of the most important properties of social networks. Detecting such structures is a challenging problem in the area of social network analysis. Community is a collection of nodes with dense connections than with the rest of the network. It is similar to clustering problem in which intra cluster edge density is more than the inter cluster edge density. Community detection algorithms are of two categories, one is disjoint community detection, in which a node can be a member of only one community at most, and the other is overlapping community detection, in which a node can be a member of more than one community. This chapter reviews the state-of-the-art disjoint and overlapping community detection algorithms. Also, the measures needed to evaluate a disjoint and overlapping community detection algorithms are discussed in detail.

Modern Technologies for Big Data Classification and Clustering - Advances in Data Mining and Database Management ◽

A Survey on Overlapping Communities in Large-Scale Social Networks

10.4018/978-1-5225-2805-0.ch008 ◽

2018 ◽

pp. 198-215

Author(s):

S Rao Chintalapudi ◽

H. M. Krishna Prasad M

Keyword(s):

Social Networks ◽

Community Detection ◽

Biological Networks ◽

Modern World ◽

Overlapping Communities ◽

Detection Algorithms ◽

Research Areas ◽

Network Generator ◽

Social network analysis is one of the emerging research areas in the modern world. Social networks can be adapted to all the sectors by using graph theory concepts such as transportation networks, collaboration networks, and biological networks and so on. The most important property of social networks is community, collection of nodes with dense connections inside and sparse connections at outside. Community detection is similar to clustering analysis and has many applications in the real-time world such as recommendation systems, target marketing and so on. Community detection algorithms are broadly classified into two categories. One is disjoint community detection algorithms and the other is overlapping community detection algorithms. This chapter reviews overlapping community detection algorithms with their strengths and limitations. To evaluate these algorithms, a popular synthetic network generator, i.e., LFR benchmark generator and the new extended quality measures are discussed in detail.

Overlapping community detection algorithm based on weak clique in multi-layer social networks

JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING ◽

10.3724/sp.j.1249.2018.04413 ◽

2018 ◽

Vol 35 (4) ◽

pp. 413

Author(s):

Yuexia ZHANG ◽

Ruiqi YANG ◽

Jin KANG

Keyword(s):

Social Networks ◽

Community Detection ◽

Detection Algorithm ◽

Overlapping Community ◽

Community Detection Algorithm

2013 IEEE 4th International Conference on Software Engineering and Service Science ◽

Overlapping community detection algorithms in complex networks based on the fuzzy spectral clustering

10.1109/icsess.2013.6615430 ◽

2013 ◽

Author(s):

Lintao Lv ◽

Weiwei Yang ◽

Yuxiang Yang ◽

fang Tan

Keyword(s):

Complex Networks ◽

Community Detection ◽

Spectral Clustering ◽

Detection Algorithms ◽

Overlapping Community Detection via Leader-Based Local Expansion in Social Networks

2012 IEEE 24th International Conference on Tools with Artificial Intelligence ◽

10.1109/ictai.2012.61 ◽

2012 ◽

Cited By ~ 1

Author(s):

Lei Pan ◽

Chao Dai ◽

Chongjun Wang ◽

Junyuan Xie ◽

Meilin Liu

Keyword(s):

Social Networks ◽

Community Detection ◽

Local Expansion ◽

Advances in Knowledge Discovery and Data Mining - Lecture Notes in Computer Science ◽

Towards Linear Time Overlapping Community Detection in Social Networks

10.1007/978-3-642-30220-6_3 ◽

2012 ◽

pp. 25-36 ◽

Cited By ~ 109

Author(s):

Jierui Xie ◽

Boleslaw K. Szymanski

Keyword(s):

Social Networks ◽

Community Detection ◽

Linear Time ◽

Advanced Methodologies and Technologies in Media and Communications - Advances in Multimedia and Interactive Technologies ◽

Aspects of Various Community Detection Algorithms in Social Network Analysis

10.4018/978-1-5225-7601-3.ch026 ◽

2019 ◽

pp. 327-341

Author(s):

Nicole Belinda Dillen ◽

Aruna Chakraborty

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Community Detection ◽

Detection Algorithms ◽

Novel Approach ◽

Recent Developments ◽

Overlapping Community ◽

Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.