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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 dynamic behavior of six real-world social and physical networks is studied and it is shown that centrality distances can be used to effectively distinguish between randomly generated and actual evolutionary paths of dynamic complex networks.
Abstract: The topological structure of complex networks has fascinated researchers for several decades, and today we have a fairly good understanding of the types and reoccurring characteristics of many different complex networks. However, surprisingly little is known today about models to compare complex graphs, and quantitatively measure their similarity and dynamics. In the past a variety of node centralities, i.e., functions which assign values to nodes to represent their importance in the graph. Based on such centralities we propose a natural similarity measure for complex networks: the centrality distance $d_C$, the difference between two graphs with respect to a given node centrality $C$. Centrality distances can take the specific roles of the different nodes in the network into account, and have many interesting applications. As a case study, we investigate the evolution of networks with respect to centrality distances and their approximations based on closeness, betweenness, pagerank, clustering and degree centrality. In particular we study the dynamic behavior of six real-world social and physical networks and show that centrality distances can be used to effectively distinguish between randomly generated and actual evolutionary paths of dynamic complex networks.

6 citations

Journal ArticleDOI
31 Jul 2015
TL;DR: To test the proposed approach, three networks have been created, which are Porto Alegre and Sioux Falls cities and a regular 10 × 10 grid, and trips were microscopically simulated and the results were compared with the proposed method.
Abstract: This paper aims to identify central points in road networks considering traffic demand. This is made with a variation of betweenness centrality. In this variation, the graph that corresponds to the road network is weighted according to the number of routes generated by the traffic demand. To test the proposed approach three networks have been created, which are Porto Alegre and Sioux Falls cities and a regular 10 × 10 grid. Then, trips were microscopically simulated and the results were compared with the proposed method

6 citations

01 Jan 2011
TL;DR: This paper implements and study betweenness centrality in the context of cloud-based platforms using Microsoft Windows Azure as a case study and demonstrates scalable parallel performance and investigates key issues related to a cloud- based implementation including mitigating penalties associated with VM failures as well as the impact of communication overheads in the cloud.
Abstract: Finding key vertices in large graphs is an important problem in many applications such as social networks, bioinformatics, and distribution networks. Betweenness centrality is a popular algorithm for finding such vertices and has been studied extensively, yielding several parallel formulations suitable to supercomputers and clusters. In this paper we implement and study betweenness centrality in the context of cloud-based platforms using Microsoft Windows Azure as our case study. We demonstrate scalable parallel performance and investigate key issues related to a cloud-based implementation including mitigating penalties associated with VM failures as well as the impact of communication overheads in the cloud. We use a combination of empirical and analytical evaluation using both synthetic small-world and real-world social interaction graphs. KeywordsGraph; Cloud computing; Azure; performance analysis; betweennness centrality; scalability

6 citations

Book
28 Apr 2006
TL;DR: In this article, a survey of 33 nonprofit organizations in the Allegheny County, Pittsburgh, Pennsylvania uncovers the hidden patterns of collaboration between the sectors including empirical evidence of blurring boundaries.
Abstract: Much of the current debates in the social service delivery have focused on the blurring boundaries between three sectors - the nonprofit, business and public sector. Surprisingly no empirical research has been given to this phenomenon from macro and comparative perspectives. First contribution of the study to is the conceptual and methodological model to link organization and strategic management theory with network theory. The study calls this new framework as collaboration network. Second, this survey of 33 nonprofit organizations in the Allegheny County, Pittsburgh, Pennsylvania uncovers the hidden patterns of collaboration between the sectors including empirical evidence of blurring boundaries. In order to reveal the hidden patterns of collaboration, the study adopts blockmodel from network analysis that is useful to reduce complex networks into concise and easily understandable forms. Major findings uncovered by network analysis are; 1) Network structures are different according to specific types of collaboration relationships. Network structures become less dense as the collaborative relationships intensify. While nonprofits do not have to spend much of their valuable resources such as time and money on maintaining informal or infrequent information sharing or work referral relations, nonprofits should commit themselves to maintaining intensive relations such as formal contract or joint program. In addition, the types of six network structures are different from each other. For example, while formal contract network is shaped as a cohesive subgroup structure, resource sharing network shows a central-periphery system. 2) When three sector organizations are participated in the work referral network, the social service system emerges. Three sectors play a unique role respectively - a sender for public agencies, a service provider for businesses. As a major actor in the social service field, nonprofits not only play these two roles, but also play a coordinating or broker role between three sectors. 3) When either of the business or public sector is introduced in the collaboration network, new network structures replace the network structure which is composed exclusively of nonprofits. For example, when the public sector is involved in the formal contract network, the network structure changes from a cohesive subgroup system to a hierarchy system.

6 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202319
202240
202175
2020109
201989
2018115