Topic
Network theory
About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.
Papers published on a yearly basis
Papers
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04 Jan 2012TL;DR: This paper establishes a relationship between information centrality and network criticality and provides a justification for using the average networkcriticality of a node to quantify the nodes relative importance in a graph.
Abstract: Network criticality (resistance distance) is a graph-theoretic metric that quantifies network robustness, and that was originally designed to capture the effect of environmental changes in core communication networks. This paper establishes a relationship between information centrality and network criticality and provides a justification for using the average network criticality of a node to quantify the nodes relative importance in a graph.This results provides a basis for designing robust clustering algorithms for vehicular networks.
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01 Jan 2015
TL;DR: In this article, the authors studied the correlation between the proven principles of network theory and the growth possibilities of the hubs (hubs) operated by logistics service providers, and provided new insights and increasing opportunities to consider the logistics systems within the field of theoretical network science.
Abstract: Logistics centres of huge capacity and having the ability to create added value have emerged in
recent decades. These centres have become network nodes between the co
-
operating
organizations which accomplish the management of supply chains (networks) b
y connecting
different modalities and networks with their infrastructure and informatics. The effective
operation of logistics centres presented in business networks are usually managed by logistics
supplier businesses (3PL/4PL). Defining the supply net as
a complex network, logistics centres
may be called hubs, the routes and relationships connecting the centres or
–
by borrowing the
term used in network theory
–
may be called edges. Hub management provides core supply
chain execution and visibility. Takin
g into consideration earlier results of network research we
are searching for correlations between its proven principles and the growth possibilities of the
centres (hubs) operated by logistics service providers. In addition, the results concerning cell
ne
tworks provide further, new insights and increasing opportunities to consider the logistics
systems within the field of theoretical network science. The present paper has developed as a
result of the logistics supplier companies’ strategic responses to the
challenges of a rapidly
changing environment, both on local and global level.
01 Mar 2012
TL;DR: This research analysed the MyExperts (Malaysian Experts Academic Social Network) in terms of the three most popular centrality measures including DegreeCentrality, Betweenness Centrality, and Closeness Centrality.
Abstract: Social network analysis (SNA) is a set of theories and technique to uncover the interaction in a social network. To understand each social network and their participants, we need to evaluate the location of actors in the network by finding the centrality of them. These measures give us insight into the various roles and groupings in a network. In this research, we analysed the MyExperts (Malaysian Experts Academic Social Network) in terms of the three most popular centrality measures including Degree Centrality, Betweenness Centrality, and Closeness Centrality. MyExperts is the first academic social network in Malaysia. It enables all universities in Malaysia to create their own social networks in which all students, graduates and researchers can have their profile and collaborate with each other. Currently, more than 600 students from 14 universities have already joined this social network and created their own profile. The database records and logs which generated with automatic log generator enabled us to access live datasets for evaluating the centrality measures in MyExperts.
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TL;DR: In this article, betweenness centrality of a vertex of a graph is defined as the portion of the shortest paths all pairs of vertices passing through a given vertex in a graph.
Abstract: A central issue in the analysis of complex networks is the assessment of their stability and vulnerability. A variety of measures have been proposed in the literature to quantify the stability of networks and a number of graph-theoretic parameters have been used to derive formulas for calculating network reliability. Different measures for graph vulnerability have been introduced so far to study different aspects of the graph behavior after removal of vertices or links such as connectivity, toughness, scattering number, binding number, residual closeness and integrity. In this paper, we consider betweenness centrality of a graph. Betweenness centrality of a vertex of a graph is portion of the shortest paths all pairs of vertices passing through a given vertex. In this paper, we obtain exact values for betweenness centrality for some wheel related graphs namely gear, helm, sunflower and friendship graphs.
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01 Nov 2012
TL;DR: The theoretical background of dynamic networks is explored and the network measures of degree, closeness, betweenness, and eigenvector centrality over time over time are used to conduct network trend analysis.
Abstract: : Modern criminal networks are constantly changing to maintain secrecy, recruit members, and coordinate activities. Attempts to uncover important elements of these networks need to incorporate dynamic trends to provide useful findings and disrupt harmful plans. Our research provides a promising approach whereby analysts can forecast network behavior and stay a step ahead of their adversaries. This report explores the theoretical background of dynamic networks and uses the network measures of degree, closeness, betweenness, and eigenvector centrality over time to conduct network trend analysis. As a case study, I examined the Ali Baba data set that provides messages from a fictitious terrorist cell over a seven-month period. The force-directed Fruchterman-Reingold algorithm was used to visualize the Ali Baba network each month to identify structure, distinguish key players, and understand behavioral roles. Despite the low density of interactions, results revealed the ranking of eigenvector centrality to match the terrorist attack cycle. Several methods for centrality measure prediction are also evaluated, including regression and moving average. Lastly, the results of the removal of a key node from a scale-free criminal network are examined. These examples are an important step in the continuing effort to predict terrorist network behavior.