scholarly journals Fast Exact and Approximate Computation of Betweenness Centrality in Social Networks

2014 ◽  
pp. 53-73 ◽  
Author(s):  
Miriam Baglioni ◽  
Filippo Geraci ◽  
Marco Pellegrini ◽  
Ernesto Lastres
Keyword(s):  
Social Networks ◽  
Betweenness Centrality ◽  
Approximate Computation

scholarly journals Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks

2019 ◽  
Vol 56 (1) ◽  
pp. 107-129
Author(s):  
James D. Westaby ◽  
Adam K. Parr
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Systems ◽  
Network Theory ◽  
Betweenness Centrality ◽  
Dynamic Network ◽  
Quadratic Assignment ◽  
Organizational Systems ◽  
Goal Striving ◽  
Shared Goal

Grounded in dynamic network theory, this study examined network goal analysis (NGA) to understand complex systems. NGA provides new insights by inserting goal nodes into social networks. Goal nodes can also represent missions, objectives, or desires, thus having wide applicability. The theory ties social networks to goal nodes through a parsimonious set of social network role linkages, such as independent goal striving, system supporting, feedback, goal preventing, supportive resisting, and system negating (i.e., those who are upset with others in the pursuit). Moreover, we extend the theory’s system reactance role linkage to better account for constructive conflicts. Two complex systems were examined: a team’s mission and an individual’s work project. In support of dynamic network theory, using the Quadratic Assignment Procedure, results demonstrated significant shared goal striving, system supporting, and shared connections between goal striving and system supporting. These findings manifest what we coin as multipendence: Systems having some actions independently involved with goals, while others are dependently involved in the associated network. NGA also demonstrated that the goal nodes manifested strong betweenness centrality, indicating that goal striving and feedback links were connecting entities across the wider system. Strategies to plan network goal interventions are illustrated with implications for practice.

scholarly journals Identifying Communication Topologies on Twitter

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2151
Author(s):  
Mijat Kustudic ◽  
Bowen Xue ◽  
Huifen Zhong ◽  
Lijing Tan ◽  
Ben Niu
Keyword(s):  
Social Networks ◽  
Betweenness Centrality ◽  
Cluster Size ◽  
Large Scale ◽  
Power Laws ◽  
Communication Patterns ◽  
Degree Centrality ◽  
General Power

Social networks are known for their decentralization and democracy. Each individual has a chance to participate and influence any discussion. Even with all the freedom, people’s behavior falls under patterns that are observed in numerous situations. In this paper, we propose a methodology that defines and searches for common communication patterns in topical networks on Twitter. We analyze clusters according to four traits: number of nodes the cluster has, their degree and betweenness centrality values, number of node types, and whether the cluster is open or closed. We find that cluster structures can be defined as (a) fixed, meaning that they are repeated across datasets/topics following uniform rules, or (b) variable if they follow an underlying rule regardless of their size. This approach allows us to classify 90% of all conversation clusters, with the number varying by topic. An increase in cluster size often results in difficulties finding topological shape rules; however, these types of clusters tend to exhibit rules regarding their node relationships in the form of centralization. Most individuals do not enter large-scale discussions on Twitter, meaning that the simplicity of communication clusters implies repetition. In general, power laws apply for the influencer connection distribution (degree centrality) even in topical networks.

scholarly journals A METHOD TO IMPROVE THE TIME OF COMPUTING BETWEENNESS CENTRALITY IN SOCIAL NETWORK GRAPH

2019 ◽  
Vol 57 (3) ◽  
pp. 344
Author(s):  
Dung Xuan Nguyen ◽  
Ban Van Doan ◽  
Ngoc Thi Bich Do
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Execution Time ◽  
Betweenness Centrality ◽  
Computing Time ◽  
Network Graph ◽  
The Social

The Betweenness centrality is an important metric in the graph theory and can be applied in the analyzing social network. The main researches about Betweenness centrality often focus on reducing the complexity. Nowadays, the number of users in the social networks is huge. Thus, improving the computing time of Betweenness centrality to apply in the social network is neccessary. In this paper, we propose the algorithm of computing Betweenness centrality by reduce the similar nodes in the graph in order to reducing computing time. Our experiments with graph networks result shows that the computing time of the proposed algorithm is less than Brandes algorithm. The proposed algorithm is compared with the Brandes algorithm [3] in term of execution time.

scholarly journals Betweenness centrality correlation in social networks

2003 ◽  
Vol 67 (1) ◽  
Author(s):  
K.-I. Goh ◽  
E. Oh ◽  
B. Kahng ◽  
D. Kim
Keyword(s):  
Social Networks ◽  
Betweenness Centrality

scholarly journals An Efficient Algorithm for Betweenness Centrality Estimation in Social Networks

2015 ◽  
Vol 4 (1) ◽  
pp. 37-44
Author(s):  
Soo-Jin Shin ◽  
Yong-Hwan Kim ◽  
Chan-Myung Kim ◽  
Youn-Hee Han
Keyword(s):  
Social Networks ◽  
Efficient Algorithm ◽  
Betweenness Centrality
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