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: This article argues that the theory of networks provides a generic theory for studying all types of connections in hydrology and explains the relevance of complex network theory for hydrologic systems.
Abstract: Connections are ubiquitous. The hydrologic cycle is perhaps the best example: every component of this cycle is connected to every other component, but some connections are stronger than the others. Unraveling the nature and extent of connections in hydrologic systems, as well as their interactions with others, has always been a fundamental challenge in hydrology. Despite the progress in this direction, a strong scientific theory that is suitable for studying all types of connections in hydrology continues to be elusive. In this article, I argue that the theory of networks provides a generic theory for studying all types of connections in hydrology. After presenting a general discussion of complex systems as networks, I offer a brief account of the history of development of network theory and some basic concepts and measures of complex networks, and explain the relevance of complex network theory for hydrologic systems, with three specific examples.
30 citations
01 Feb 2015
TL;DR: Eigenvector centrality is more suited than other centrality measures for finding prominent or key author in research professionals' relationship network and its application based on Network x is discussed.
Abstract: The centrality of vertices has been the key issue in social network analysis. Many centrality measures have been presented, such as degree, closeness, between's and eigenvector centrality. But eigenvector centrality is more suited than other centrality measures for finding prominent or key author in research professionals' relationship network. In this paper, we discuss eigenvector centrality and its application based on Network x. In eigenvector centrality first set every node a starting amount of influence then performs power iteration method. In network x the starting amount of influence of each node is 1/len(G). Therefore, we modify the eigenvector centrality algorithm and set the starting amount of influence of each node is the degree centrality of that node because eigenvector centrality is the extension of degree centrality and also implements the eigenvector centrality in weighted network.
30 citations
TL;DR: The development of NEXCADE is reported, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation.
Abstract: Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the ‘robust, yet fragile’ nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS) can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html.
30 citations
TL;DR: The curvilinear (inverted U-shaped) association of direct and distant co-authorship ties (degree centrality) with research performance with formal rank having a positive moderating role for lower ranked faculty is revealed.
Abstract: This study explores the curvilinear (inverted U-shaped) association of three classical dimension of co-authorship network centrality, degree, closeness and betweenness and the research performance in terms of g-index, of authors embedded in a co-authorship network, considering formal rank of the authors as a moderator between network centrality and research performance. We use publication data from ISI Web of Science (from years 2002---2009), citation data using Publish or Perish software for years 2010---2013 and CV's of faculty members. Using social network analysis techniques and Poisson regression, we explore our research questions in a domestic co-authorship network of 203 faculty members publishing in Chemistry and it's sub-fields within a developing country, Pakistan. Our results reveal the curvilinear (inverted U-shaped) association of direct and distant co-authorship ties (degree centrality) with research performance with formal rank having a positive moderating role for lower ranked faculty.
30 citations
TL;DR: This study shows a method based on network theory to identify the most important journals related to a given journal, the seed journal, suggesting influence relations between journals in such a way that traditional field boundaries are transcended.
Abstract: The study presented here shows a method based on network theory to identify the most important journals related to a given journal, the seed journal. In just one simple network map, we get the relevant citation environment of a specific seed journal. It is of interest to librarians, publishers, scientists and science policy makers. These journal citation network maps are useful for these various stakeholders in and around the science system, as they provide information on the level of journal connections, unlike the more traditional structures such as the Journal Subject Categories, the classification system applied in the products of Thomson Reuters (Journal Citation Reports, Web of Science, etc.). These network maps show the closest relations journals can have, based on citation relations, suggesting influence relations between journals in such a way that traditional field boundaries are transcended.
30 citations