Topic
Network theory
About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.
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TL;DR: The theoretical underpinning of the analysis is reviewed and some the main properties such as indirect effects ratio, network homogenization, and network mutualism are introduced.
Abstract: Network Environ Analysis, based on network theory, reveals the quantitative and qualitative relations between ecological objects interacting with each other in a system. The primary result from the method provides input and output “environs”, which are internal partitions of the objects within system flows. In addition, application of Network Environ Analysis on empirical datasets and ecosystem models has revealed several important and non-intuitive results that have been identified and summarized in the literature as network environ properties. Network Environ Analysis requires data including the inter-compartmental flows, compartmental storages, and boundary input and output flows. Software is available to perform this analysis on the collected data. This article reviews the theoretical underpinning of the analysis and briefly introduces some the main properties such as indirect effects ratio, network homogenization, and network mutualism.
6 citations
TL;DR: There is a good case for reconceptualizing comorbid psychopathologies in terms of complex network theory and a neglected approach to theory appraisal that might usefully be incorporated into the methodology of network theory.
Abstract: Cramer et al. make a good case for reconceptualizing comorbid psychopathologies in terms of complex network theory. We suggest the need for an extension of their network model to include reference to latent causes. We also draw attention to a neglected approach to theory appraisal that might usefully be incorporated into the methodology of network theory.
6 citations
24 Mar 2014
TL;DR: This paper focuses the problem of the evaluation of the centrality of a node in a Distributed Online Social Network and proposes a distributed approach for the computation of the Ego Betweenness Centrality, which is an ego-centric method to approximate the BetweennessCentrality.
Abstract: Online Social Networks (OSNs) usually exploit a logically centralized infrastructure which has several drawbacks including scalability, privacy, and dependence on a provider. In contrast to centralized OSNs, a Distributed Online Social Network helps to lower the cost of the provider drastically, and allows better control of user privacy. A distributed approach introduces new problems to address, as data availability or information diffusion, which require the definition of methods for the analysis of the social graph. This paper focuses the problem of the evaluation of the centrality of a node in a Distributed Online Social Network and proposes a distributed approach for the computation of the Ego Betweenness Centrality, which is an ego-centric method to approximate the Betweenness Centrality. We propose a set of algorithms to compute the betweenness centrality in static and dynamic graphs, which can be directed or undirected. We propose both a broadcast and a gossip protocol to compute the Ego Betweenness Centrality. A set of experimental results proving the effectiveness of our approach are presented.
6 citations
14 Mar 2016
TL;DR: A new between-ness centrality algorithm with local search called BCALS is proposed as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process.
Abstract: Community structure identification in complex networks has been an important research topic in recent years. In this paper, a new between-ness centrality algorithm with local search called BCALS in short, is proposed as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. BCALS selects at first, leaders according to their measure of between-ness centrality, then it selects randomly a node and calculates its local function for all communities and assigns it to the community that optimizes its local function. Experiments show that BCALS gets effective results compared to other detection community algorithms found in the literature.
6 citations
01 Jan 2006
TL;DR: This paper addresses the hierarchy property sharing among a large amount of networks and proposes a method to discover the hierarchical structure of a terrorist network based upon the degree centrality and eigenvector centrality measure.
Abstract: The network structure of a group determines its strengths and weaknesses. A general knowledge of the prevalent models of terrorist organizations and its analysis methods leads to a better understanding of their capabilities. One important method is centrality analysis, which determines the relative importance of vertices (terrorists) in a network based on their connectivity within the network structure. In this paper, we address the hierarchy property sharing among a large amount of networks. Based upon the degree centrality (DC) and eigenvector centrality (EC) measure, a method is proposed to discover the hierarchical structure of a terrorist network. This hierarchical structure sheds light on the leadership and the likely sub groups embedded in the network
6 citations