Criminals Detection in Social Networks Using Centrality Measures Algorithm

2021 ◽  
Vol 7 (4) ◽  
pp. 378
Author(s):  
Zeinab Alebouyeh ◽  
Amir Bidgoly
Keyword(s):  
Social Networks ◽  
Centrality Measures

scholarly journals Topological measures for identifying and predicting the spread of complex contagions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Douglas Guilbeault ◽  
Damon Centola
Keyword(s):  
Social Networks ◽  
Shortest Path ◽  
Empirical Data ◽  
Path Length ◽  
Centrality Measures ◽  
Social Phenomena ◽  
Node Centrality ◽  
Network Connectedness ◽  
Topological Measures ◽  
Definition Of

AbstractThe standard measure of distance in social networks – average shortest path length – assumes a model of “simple” contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are “complex” contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.

scholarly journals A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Chengying Mao ◽  
Weisong Xiao
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Preference Relation ◽  
Disease Outbreak ◽  
Centrality Measures ◽  
The Internet ◽  
Virus Propagation ◽  
Preference Graph ◽  
Partial Preference ◽  
Relation Analysis

In the era of big data, social network has become an important reflection of human communications and interactions on the Internet. Identifying the influential spreaders in networks plays a crucial role in various areas, such as disease outbreak, virus propagation, and public opinion controlling. Based on the three basic centrality measures, a comprehensive algorithm named PARW-Rank for evaluating node influences has been proposed by applying preference relation analysis and random walk technique. For each basic measure, the preference relation between every node pair in a network is analyzed to construct the partial preference graph (PPG). Then, the comprehensive preference graph (CPG) is generated by combining the preference relations with respect to three basic measures. Finally, the ranking of nodes is determined by conducting random walk on the CPG. Furthermore, five public social networks are used for comparative analysis. The experimental results show that our PARW-Rank algorithm can achieve the higher precision and better stability than the existing methods with a single centrality measure.

Classifying the Influential Individuals in Multi-Layer Social Networks

2019 ◽  
Vol 8 (1) ◽  
pp. 21-32
Author(s):  
Ruchi Mittal ◽  
M.P.S Bhatia
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Real Life ◽  
Single Layer ◽  
Machine Learning Techniques ◽  
Centrality Measures ◽  
Learning Techniques ◽  
Modes Of Interaction ◽  
Source Of Information

Nowadays, social media is one of the popular modes of interaction and information diffusion. It is commonly found that the main source of information diffusion is done by some entities and such entities are also called as influencers. An influencer is an entity or individual who has the ability to influence others because of his/her relationship or connection with his/her audience. In this article, we propose a methodology to classify influencers from multi-layer social networks. A multi-layer social network is the same as a single layer social network depict that it includes multiple properties of a node and modeled them into multiple layers. The proposed methodology is a fusion of machine learning techniques (SVM, neural networks and so on) with centrality measures. We demonstrate the proposed algorithm on some real-life networks to validate the effectiveness of the approach in multi-layer systems.

The Role of Social Networking in Capital Sourcing

2019 ◽  
pp. 097215091986886 ◽  
Author(s):  
Ameeta Jaiswal-Dale ◽  
Fanny Simon-Lee ◽  
Giovanna Zanotti ◽  
Peter Cincinelli
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Early Stage ◽  
Centrality Measures ◽  
Spontaneous Growth ◽  
Empirical Results ◽  
Road Map ◽  
The Social

The aim of this research is to apply the tool of social network analysis to situations in capital sourcing, including early stage financing. The study is conducted within the social network of Medical Alley Association of Minnesota (MAA). We investigate the correlation between the main centrality measures: closeness, degree and betweenness, and the amount of funding received by the 163 MAA members during 2009–2012. Companies benefit from their social network to get access to better financing. The empirical results also provide a road map to encourage the sponsored or spontaneous growth of other social networks in related fields. Despite the financial crisis, the empirical results show how competition works when firms have established relations with others. Where an intersection occurs is merely an empirical curiosity and the causation resides in the intersection of relations. The relation that intersects on an organization determines the player’s competitive advantage.

scholarly journals Twitter Networks’ Centrality Measures of the Association of Health Journalist (ANIS): the Case of Vaccines - Introduction and Theoretical Framework

2020 ◽  
Vol 3 (2) ◽  
pp. 12
Author(s):  
Miguel Martín Cárdaba ◽  
Rafael Carrasco Polaino ◽  
Ubaldo Cuesta Cambra
Keyword(s):  
Social Networks ◽  
Public Opinion ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Theoretical Framework ◽  
Communication Strategy ◽  
Centrality Measures ◽  
The Social ◽  
One Year

The popularization of Internet and the rise of social networks have offered an unbeatable opportunity for anti-vaccines, especially active communicators, to spread their message more effectively causing a significant impact on public opinion. A great amount of research has been carried out to understand the behavior that anti-vaccine communities show on social networks. However, it seems equally relevant to study the behavior that communities and communicators “pro vaccines” perform in these networks. Therefore, the objective of this research has been to study how members of the Spanish Association of Health Journalist (ANIS) communicate and use the social network Twitter. More specifically, the different interactions made by ANIS partners were analyzed through the so-called “centrality measures of social network analysis” (SNA), to check the configuration of the user network and detect those most relevant by their indexes of centrality, intermediation or mentions received. The research monitored 142 twitter accounts for one year analyzing 254 twits and their 2.671 interactions. The research concluded that the network of ANIS partners on Twitter regarding vaccines has little cohesion and has several components not connected to each other, in addition to the fact that there are users outside the association that show greater relevance than the partners themselves. We also concluded that there are an important lack of planning and direction in the communication strategy of ANIS on Twitter, which limits the dissemination of important content.

Processing Evolving Social Networks for Change Detection Based on Centrality Measures

2018 ◽  
pp. 155-176 ◽  
Author(s):  
Fabíola S. F. Pereira ◽  
Shazia Tabassum ◽  
João Gama ◽  
Sandra de Amo ◽  
Gina M. B. Oliveira
Keyword(s):  
Social Networks ◽  
Change Detection ◽  
Centrality Measures

Social Networks and Computer Mediated Communication

2012 ◽  
pp. 727-737 ◽  
Author(s):  
Romana Xerez ◽  
Paulo Figueiredo ◽  
Miguel Mira da Silva
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Collaborative Work ◽  
Computer Mediated Communication ◽  
Centrality Measures ◽  
Mediated Communication ◽  
Phone Calls ◽  
Computer Mediated ◽  
The Impact

This chapter examines social networks in the Portuguese society, and the impact of these social networks on organizations regarding Computer-Mediated Communication. The results describe a Portuguese case study and attempt to answer the following question: How does Computer-Mediated Communication contribute to social networking in organizations? This chapter examines the emails and phone calls exchanged during the year 2008 by employees working for a Portuguese bank in order to identify nodes, roles, positions, types of relations, types of networks and centrality measures. Overall there were 93.654 internal calls and 542.674 emails exchanged between the actors. The findings suggest that emailing is the preferred means of communication, although frequency increases with hierarchy communication. Collaborative work between departments functions as the emergence of a network. The results confirm the relevance of computer networks to support social networks in organizations, and its potential concerning data analysis outside the traditional surveys, and the possibility of introducing Internet sources.

scholarly journals INTERVENTIONS IN SOCIAL NETWORKS: IMPACT ON MOOD AND NETWORK DYNAMICS

2015 ◽  
Vol 18 (03n04) ◽  
pp. 1550016 ◽  
Author(s):  
DANICA VUKADINOVIĆ GREETHAM ◽  
ABHIJIT SENGUPTA ◽  
ROBERT HURLING ◽  
JOY WILKINSON
Keyword(s):  
Social Networks ◽  
Negative Affect ◽  
Positive Affect ◽  
Rate Of Change ◽  
Centrality Measures ◽  
Positive And Negative Affect ◽  
Friendship Networks ◽  
Gratitude Intervention ◽  
High Level ◽  
The Impact

Results from two studies on longitudinal friendship networks are presented, exploring the impact of a gratitude intervention on positive and negative affect dynamics in a social network. The gratitude intervention had been previously shown to increase positive affect and decrease negative affect in an individual but dynamic group effects have not been considered. In the first study, the intervention was administered to the whole network. In the second study, two social networks are considered and in each only a subset of individuals, initially low/high in negative affect respectively received the intervention as "agents of change". Data was analyzed using stochastic actor-based modeling techniques to identify resulting network changes, impact on positive and negative affect and potential contagion of mood within the group. The first study found a group level increase in positive and a decrease in negative affect. Homophily was detected with regard to positive and negative affect but no evidence of contagion was found. The network itself became more volatile along with a fall in rate of change of negative affect. Centrality measures indicated that the best broadcasters were the individuals with the least negative affect levels at the beginning of the study. In the second study, the positive and negative affect levels for the whole group depended on the initial levels of negative affect of the intervention recipients. There was evidence of positive affect contagion in the group where intervention recipients had low initial level of negative affect and contagion in negative affect for the group where recipients had initially high level of negative affect.

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