Community Detection in Social Networks: Literature Review

2019 ◽  
Vol 18 (02) ◽  
pp. 1950019 ◽  
Author(s):  
Seema Rani ◽  
Monica Mehrotra
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Geographic Location ◽  
Cost Effective ◽  
Research Areas ◽  
The Social ◽  
Intensive Investigation ◽  
Optimisation Techniques

Due to easy and cost-effective ways, communication has amplified many folds among humans across the globe irrespective of time and geographic location. This has led to the construction of an enormous and a wide variety of social networks that is a network of social interactions or personal relations. Social network analysis (SNA) is the inspection of social networks in order to understand the participant’s arrangement and behaviour. Discovering communities from the social network has become one of the key research areas in SNA. Communities discovered from social networks facilitate its members so as to interact with relatable people who have similar or comparable interests. However, in present time, the enormous growth of social networks demands an intensive investigation of recent work carried out for identifying community division in social networks. This paper is an attempt to enlighten the ongoing developments in the domain of Community detection (CD) for SNA. Additionally, it sheds light on the algorithms which use meta-heuristic optimisation techniques to hit upon the community structure in social networks. Further, this paper gives a comparison of proposed methods in recent years and most frequently used optimisation approaches in the domain of CD. It also describes some application areas where CD methods have been used. This guides and encourages researchers to probe and take ahead the work in the area of detecting communities from social networks.

scholarly journals An efficient method to detect communities in social networks

2021 ◽  
Author(s):  
MEHJABIN KHATOON ◽  
W AISHA BANU
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Average Degree ◽  
Data Sets ◽  
Time Data ◽  
Network Graph ◽  
The Social ◽  
Metric Functions

Abstract Social networks represent the social structure, which is composed of individuals having social interactions among them. The interactions between the units in a social network represent the relations of the various social contacts and aim at finding different individuals in that network, with similar interests. It is a challenging problem to detect the social interactions between individuals with comparable considerations and desires from a large social network, which can be termed as community detection. Detection of the communities from social networks has been done by other authors previously, and many community identification algorithms were also proposed, but those communities' identification has been achieved on the online available data sets. The proposed algorithm in this paper has been named as Average Degree Newman Girvan (ADNG) algorithm, which can easily identify the communities from the real-time data sets, collected from the social network websites. The approach presented here is based on first determining the average degree of the network graph and then identifying the communities using the Newman Girvan algorithm. The proposed algorithm has been compared with four community detection algorithms, i.e., Leading eigenvector (LEC) algorithm, Fastgreedy (FG) algorithm, Leiden algorithm and Kernighan-Lin (KL) algorithm based on a few metric functions. This algorithm helps to detect communities for different domains, like for any proposed government policy, online shopping products, newly launched products in a market, etc.

scholarly journals An Efficient Method to Detect Communities in Social Networks

2020 ◽  
Author(s):  
MEHJABIN KHATOON ◽  
W. Aisha Banu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Average Degree ◽  
Data Sets ◽  
Time Data ◽  
Network Graph ◽  
The Social ◽  
Metric Functions

Abstract Social networks represent the social structure, which is composed of individuals having social interactions among them. The interactions between the units in a social network represent the relations of the various social contacts and aim at finding different individuals in that network, with similar interests. It is a challenging problem to detect the social interactions between individuals with comparable considerations and desires from a large social network, which can be termed as community detection. Detection of the communities from social networks has been done by other authors previously, and many community identification algorithms were also proposed, but those communities' identification has been achieved on the online available data sets. The proposed algorithm in this paper has been named as Average Degree Newman Girvan (ADNG) algorithm, which can easily identify the communities from the real-time data sets, collected from the social network websites. The approach presented here is based on first determining the average degree of the network graph and then identifying the communities using the Newman Girvan algorithm. The proposed algorithm has been compared with three community detection algorithms, i.e., Leading eigenvector (LEC) algorithm, Fastgreedy (FG) algorithm, and Kernighan-Lin (KL) algorithm based on a few metric functions. This algorithm helps to detect communities for different domains, like for any proposed government policy, online shopping products, newly launched products in a market, etc.

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.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

Networks of Co-Authorship

2012 ◽  
pp. 981-994
Author(s):  
Antonio José Caulliraux Pithon ◽  
Ralfh Varges Ansuattigui ◽  
Paulo Enrique Stecklow
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Morphological Characteristics ◽  
Network Structures ◽  
Communication Processes ◽  
The Social ◽  
The Subject

The networks are transorganizational arrangements forming a structure and, in a more abstract and generic manner, are built from the interactions between individuals and organizations. These interactions allow the emergence of network structures more related to personal ties and the types of existing social interactions between the actors. Social networks aren’t a recent enterprise, but have been the subject of deeper studies due to universalization and convergence of communication processes, fundamental to the establishment and proliferation of networks. The structure where networks are manifested calls for horizontality, where there is no formal hierarchy of the elements that comprise it, composed by nodes elements and lines elements. This article analyzes the social network of authorship of one of five Postgraduate Programs of CEFET/RJ, presenting the connections between network teachers, justifying the morphological characteristics of the network and suggesting methodologies for continuing the study for the teaching and researching networks.

scholarly journals Social Network Community Detection by Combining Self-Organizing Maps and Genetic Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mehdi Ellouze
Keyword(s):  
Social Networks ◽  
Genetic Algorithms ◽  
Social Network ◽  
Community Detection ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
The Social ◽  
Two Stages ◽  
Self Organizing

Social networks have become an important source of information from which we can extract valuable indicators that can be used in many fields such as marketing, statistics, and advertising among others. To this end, many research works in the literature offer users some tools that can help them take advantage of this mine of information. Community detection is one of these tools and aims to detect a set of entities that share some features within a social network. We have taken part in this effort, and we proposed an approach mainly based on pattern recognition techniques. The novelty of this approach is that we do not directly tackle the social networks to find these communities. We rather proceeded in two stages; first, we detected community cores through a special type of self-organizing map called the Growing Hierarchical Self-Organizing Map (GHSOM). In the second stage, the agglomerations resulting from GHSOM were grouped to retrieve the final communities. The quality of the final partition would be under the control of an evaluation function that is maximized through genetic algorithms. Our system was tested on real and artificial databases, and the obtained results are really encouraging.

scholarly journals Correlation analysis of community detection in social network of big data methodical using set theorem

2018 ◽  
Vol 7 (2.21) ◽  
pp. 398
Author(s):  
K M. Monica ◽  
R Parvathi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Detection Method ◽  
Literature Survey ◽  
Network System ◽  
Research Fields ◽  
The Social ◽  
Basic Concepts ◽  
Ultimate Objective

A trending issue in the network system that aids in learning and understanding the overall network structure is the community detection in the social network. Actually, they are the dividing wall which divides the node of the network into several subgroups. While dividing, the nodes within the subgroups will get connected densely but, their connections will be sparser between the subgroups. The ultimate objective of the community detection method is to divide the network into dense regions of the graph. But, in general, those regions will correlate with close related entities which can be then said that it is belonging to a community. It is defined based on the principle that the pair of nodes will be connected only if they belong to the same community and if they don’t share the communities, they are less likely to be connected. The vital problems across various research fields like the detection of minute and scattered communities have been necessitated with the ever growing variety of the social networks. The problem of community detection over the time has been recognized with the literature survey and the proposal methodology of set theorem to find the communities detection where the group belongs to activities. In addition to this, several basic concepts are stated in an exhaustive way where the research fields arise from social networks.  

scholarly journals Persistent Political Engagement: Social Interactions and the Dynamics of Protest Movements

2021 ◽  
Vol 3 (2) ◽  
pp. 233-250
Author(s):  
Leonardo Bursztyn ◽  
Davide Cantoni ◽  
David Y. Yang ◽  
Noam Yuchtman ◽  
Y. Jane Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Political Engagement ◽  
Political Movements ◽  
Political Movement ◽  
Protest Movements ◽  
Persistent Effect ◽  
The Social

We study the causes of sustained participation in political movements. To identify the persistent effect of protest participation, we randomly indirectly incentivize Hong Kong university students into participation in an antiauthoritarian protest. To identify the role of social networks, we randomize this treatment’s intensity across major-cohort cells. We find that incentives to attend one protest within a political movement increase subsequent protest attendance but only when a sufficient fraction of an individual’s social network is also incentivized to attend the initial protest. One-time mobilization shocks have dynamic consequences, with mobilization at the social network level important for sustained political engagement. (JEL D72, D74, I23, Z13)

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

Sign in / Sign up

Export Citation Format

Share Document