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 Social Network and Healthy Ageing among Elderly in Helping Hearts Old Age Home, Coimbatore

2019 ◽  
Vol 8 (12S) ◽  
pp. 571-576
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Participation ◽  
Meaning In Life ◽  
Well Being ◽  
The Elderly ◽  
Healthy Ageing ◽  
The Social ◽  
The Relationship

In the current times, the research cites that elderly definitely need social networks to aid in their mental and physical well being. The previous researches have indicated familyfocused, friend-focused, and restricted types as the types of social networks available. Social network include social interaction and social communication. It is the need of the hour to study about the social network of the elderly population because many of them are left with nobody and loneliness is one of the important factor not to mention about desertion by their loved ones since they are no longer productive individuals. The heterogeneity of social networks is pathway to successful and healthy ageing. Healthy ageing is about using opportunities so that they can have social participation and lead a good quality of life. Elderly need not be burdensome individuals in the society instead they can be involved in lot of activities which contribute to them ageing gracefully. The research studies state that rural elderly have more chances of social participation that they find more meaning in life which is a contributing factor for healthy ageing. The present study aims to find out the relationship between social network and healthy ageing.

Preventing Identity Disclosure in Social Networks Using Intersected Node

2016 ◽  
Vol 10 (3) ◽  
pp. 25-41 ◽  
Author(s):  
Amardeep Singh ◽  
Divya Bansal ◽  
Sanjeev Sofat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Privacy And Security ◽  
Identity Disclosure ◽  
The Social ◽  
Confidential Data ◽  
Noise Nodes ◽  
Graphical Structure

Social networks like Facebook, Twitter, Pinterest etc. provide data of its users to the demanding organizations to better comprehend the quality of their potential clients. Publishing confidential data of social network users in its raw form raises several privacy and security concerns. Recently, some anonymization techniques have been developed to address these issues. In this paper, a technique to prevent identity disclosure through structure attacks has been proposed which not only prevents identity disclosure but also preserves utility of data published by online social networks. Algorithms have been developed by using noise nodes/edges with the consideration of introducing minimum change in the original graphical structure of social networks. The authors' work is unique in the sense that previous works are based on edge editing only but their proposed work protects against structure attacks using mutual nodes in the social network and the effectiveness of the proposed technique has been proved using APL (Average Path Length) and information loss as parameters.

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.

Personality and social network effects on romantic relationships: a dyadic approach

2004 ◽  
Vol 18 (4) ◽  
pp. 279-299 ◽  
Author(s):  
Franz J. Neyer ◽  
Daniel Voigt
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Romantic Relationships ◽  
Personality Traits ◽  
Relationship Quality ◽  
Network Effects ◽  
Perceived Quality ◽  
The Social ◽  
Social Network Effects

The quality of romantic relationships and their associations with both partners' personality traits and social networks were studied in 100 younger couples. The similarity of partners was modest with respect to personality traits, and moderate to large with respect to the perceived quality of the partner relationship and their social networks. While similarity in personality was unrelated to relationship quality, dyadic analyses showed that one's perceived quality of relationship was better predicted by one's own personality (i.e. actor effects) than by the personality of one's partner (i.e. partner effects). Moreover, relationship quality could to some extent be predicted by the quality of the social network once the personality traits of each partner were controlled. Results are discussed from a transactional view of personality and relationships. Copyright © 2004 John Wiley & Sons, Ltd.

Work relationships: counteracting the negative effects of conflict

2016 ◽  
Vol 27 (2) ◽  
pp. 225-248 ◽  
Author(s):  
Martijn Jungst ◽  
Boris Blumberg
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Job Engagement ◽  
Content Type ◽  
Moderated Mediation Model ◽  
The Social ◽  
Network Quality ◽  
And Performance ◽  
The Relationship

Purpose Guided by social resource theory, this study aims to examine the influence of conflict (i.e. task and relationship) on performance. The authors investigated whether job engagement mediates this relationship and whether social network quality moderates the relationship between conflict, job engagement and performance. Design/methodology/approach The authors built and tested a moderated mediation model, using data from 217 graduate students. Findings Results showed that job engagement operates as a mediating mechanism between task conflict and performance. The authors also found that the indirect effect of job engagement depended upon the quality of the social networks. When the quality of the social network was high, both the task and relationship conflict did not negatively influence the association between job engagement and performance. Research limitations/implications These findings provide new insights into how social embeddedness in the form of social network quality can create a social context in which conflict works out less detrimental. Practical implications Given that employees are interdependent and coworkers are likely to differ in their personal values and opinions, the authors conclude that managers should facilitate the development of meaningful relationships at work. Originality/value Whereas prior research has found conflict (i.e. task and relationship) to negatively associate with performance, the authors show that social networks do affect the strength of the relationship between conflict (i.e. task and relationship) and performance.

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 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 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.

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.

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