Community detection in social networks

2017 ◽  
Vol 01 (01) ◽  
pp. 1630001 ◽  
Author(s):  
Hossein Fani ◽  
Ebrahim Bagheri
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Computer Science ◽  
Community Detection ◽  
Online Social Networks ◽  
Online Community ◽  
Detection Methods ◽  
Fundamental Part ◽  
Social Settings ◽  
Online Experience

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.

scholarly journals Geographical Structural Features of the WeChat Social Networks

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.

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

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.

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

Spanning tree-based fast community detection methods in social networks

2015 ◽  
Vol 11 (3) ◽  
pp. 177-186 ◽  
Author(s):  
Partha Basuchowdhuri ◽  
Riya Roy ◽  
Siddhartha Anand ◽  
Diksha Roy Srivastava ◽  
Subhashis Majumder ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Spanning Tree ◽  
Detection Methods

Community Detection Approaches in Real World Networks

2014 ◽  
Vol 6 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Pooja Wadhwa ◽  
M.P.S Bhatia
Keyword(s):  
Community Detection ◽  
Online Social Networks ◽  
Temporal Dynamics ◽  
Detection Methods ◽  
Unified Approach ◽  
Detection Algorithms ◽  
Dynamic Community Detection ◽  
Dynamic Communities ◽  
Dynamic Community

Online social networks have been continuously evolving and one of their prominent features is the evolution of communities which can be characterized as a group of people who share a common relationship among themselves. Earlier studies on social network analysis focused on static network structures rather than dynamic processes, however, with the passage of time, the networks have also evolved and the researchers have started to focus on the aspect of studying dynamic behavior of networks. This paper aims to present an overview of community detection approaches graduating from static community detection methods towards the methods to identify dynamic communities in networks. The authors also present a classification of the existing dynamic community detection algorithms along the dimension of studying the evolution as either a two-step approach comprising of community detection via static methods and then applying temporal dynamics or a unified approach which comprises of dynamic detection of communities along with their evolutionary characteristics.

Community Detection in Online Social Networks: A Differentially Private and Parsimonious Approach

2020 ◽  
Vol 7 (1) ◽  
pp. 151-163 ◽  
Author(s):  
Tianxi Ji ◽  
Changqing Luo ◽  
Yifan Guo ◽  
Qianlong Wang ◽  
Lixing Yu ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks
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