Spanning tree-based fast community detection methods in social networks

Online social networks have become a fundamental part of the global online experience. They facilitate different modes of communication and social interactions, enabling individuals to play social roles that they regularly undertake in real social settings. In spite of the heterogeneity of the users and interactions, these networks exhibit common properties. For instance, individuals tend to associate with others who share similar interests, a tendency often known as homophily, leading to the formation of communities. This entry aims to provide an overview of the definitions for an online community and review different community detection methods in social networks. Finding communities are beneficial since they provide summarization of network structure, highlighting the main properties of the network. Moreover, it has applications in sociology, biology, marketing and computer science which help scientists identify and extract actionable insight.

Download Full-text

Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

International Journal of Operations Research and Information Systems ◽

10.4018/ijoris.20210401.oa2 ◽

2021 ◽

Vol 12 (2) ◽

pp. 15-32

Author(s):

Ehsan Ardjmand ◽

William A. Young II ◽

Najat E. Almasarwah

Keyword(s):

Social Networks ◽

Complex Networks ◽

Community Detection ◽

Optimization Problems ◽

Search Algorithm ◽

Academic Community ◽

Detection Methods ◽

The Unconscious ◽

Comprehensive Review ◽

Wide Range

Detecting the communities that exist within complex social networks has a wide range of application in business, engineering, and sociopolitical settings. As a result, many community detection methods are being developed by researchers in the academic community. If the communities within social networks can be more accurately detected, the behavior or characteristics of each community within the networks can be better understood, which implies that better decisions can be made. In this paper, a discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods. The comparative study shows that the unconscious search algorithm consistently produced the highest modularity that was discovered through the comprehensive review of the literature.

Download Full-text

Geographical Structural Features of the WeChat Social Networks

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9050290 ◽

2020 ◽

Vol 9 (5) ◽

pp. 290

Author(s):

Chuan Ai ◽

Bin Chen ◽

Hailiang Chen ◽

Weihui Dai ◽

Xiaogang Qiu

Keyword(s):

Social Networks ◽

Communication Networks ◽

Community Detection ◽

Online Social Networks ◽

Statistical Models ◽

Interaction Analysis ◽

Structural Features ◽

Detection Algorithm ◽

Detection Methods ◽

Characteristics Analysis

Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.

Download Full-text

A survey on community detection methods based on the nature of social networks

ICCKE 2013 ◽

10.1109/iccke.2013.6682855 ◽

2013 ◽

Cited By ~ 6

Author(s):

Maryam pourkazemi ◽

Mohammadreza Keyvanpour

Keyword(s):

Social Networks ◽

Community Detection ◽

Detection Methods

Download Full-text

Modern Community Detection Methods in Social Networks

Science and Education of the Bauman MSTU ◽

10.7463/0417.0001133 ◽

2017 ◽

Vol 17 (04) ◽

pp. 137-152 ◽

Cited By ~ 1

Author(s):

V Chesnokov ◽

P Klyucharev

Keyword(s):

Social Networks ◽

Community Detection ◽

Detection Methods

Download Full-text

A classification of community detection methods in social networks: a survey

International Journal of General Systems ◽

10.1080/03081079.2020.1863394 ◽

2021 ◽

pp. 1-29

Author(s):

S. Souravlas ◽

A. Sifaleras ◽

M. Tsintogianni ◽

S. Katsavounis

Keyword(s):

Social Networks ◽

Community Detection ◽

Detection Methods

Download Full-text

Community Detection Using Node Attributes and Structural Patterns in Online Social Networks

Computer and Information Science ◽

10.5539/cis.v10n4p50 ◽

2017 ◽

Vol 10 (4) ◽

pp. 50

Author(s):

Bikash Chandra Singh ◽

Mohammad Muntasir Rahman ◽

Md Sipon Miah ◽

Mrinal Kanti Baowaly

Keyword(s):

Social Networks ◽

Community Detection ◽

Online Social Networks ◽

Online Social Network ◽

Detection Methods ◽

Structural Patterns ◽

Detection Algorithms ◽

Proposed Model ◽

Node Attributes ◽

Overlapping Community

Community detection in online social networks is a difficult but important phenomenon in term of revealing hidden relationships patterns among people so that we can understand human behaviors in term of social-economics perspectives. Community detection algorithms allow us to discover these types of patterns in online social networks. Identifying and detecting communities are not only of particular importance but also have immediate applications. For this reason, researchers have been intensively investigated to implement efficient algorithms to detect community in recent years. In this paper, we introduce set theory to address the community detection problem considering node attributes and network structural patterns. We also formulate probability theory to detect the overlapping community in online social network. Furthermore, we extend our focus on the comparative analysis on some existing community detection methods, which basically consider node attributes and edge contents for detecting community. We conduct comprehensive analysis on our framework so that we justify the performance of our proposed model. The experimental results show the effectiveness of the proposed approach.

Download Full-text