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: In this article , a new centrality measure based on random walk betweenness is proposed to increase the ranking of nodes belonging to dense clusters with a higher average degree than the remaining clusters.
3 citations
02 Jun 2008
TL;DR: This paper explores the information network metrics and network theory of a systems-of-systems communication netcentric in a warfare environment and develops Matlab simulations to demonstrate the concepts and quantify the network metrics discussed for important information communication system configurations.
Abstract: This paper explores the information network metrics and network theory of a systems-of-systems communication netcentric in a warfare environment. It begins with a discussion of the relationship between the network space and the battlespace. Matlab simulations are developed to demonstrate the concepts and quantify the network metrics discussed for important information communication system configurations. The effect of electronic attack is also addressed.
3 citations
07 Dec 2013
TL;DR: This work proposes data mining approaches for identification and examination of relationships between hub and driver nodes within human, yeast, rat, and mouse PPI networks and shows that identifying and cross-referencing different types of topologically significant nodes can exemplify properties such as transcription factor enrichment, lethality, clustering, and Gene Ontology enrichment.
Abstract: Network theory has been used for modeling biological data as well as social networks, transportation logistics, business transcripts, and many other types of data sets. Identifying important features/parts of these networks for a multitude of applications is becoming increasingly significant as the need for big data analysis techniques grows. When analyzing a network of protein-protein interactions (PPIs), identifying nodes of significant importance can direct the user toward biologically relevant network features. In this work, we propose that a node of structural importance in a network model can correspond to a biologically vital or significant property. This relationship between topological and biological importance can be seen in/between structurally defined nodes, such as hub nodes and driver nodes, within a network and within clusters. This work proposes data mining approaches for identification and examination of relationships between hub and driver nodes within human, yeast, rat, and mouse PPI networks. Relationships with other types of significant nodes, with direct neighbors, and with the rest of the network were analyzed to determine if the model can be characterized biologically by its structural makeup. We performed numerous tests on structure with a data-driven mentality, looking for properties that were potentially significant on a network level and then comparing those properties to biological significance. Our results showed that identifying and cross-referencing different types of topologically significant nodes can exemplify properties such as transcription factor enrichment, lethality, clustering, and Gene Ontology (GO) enrichment. Mining the biological networks, we discovered a key relationship between network properties and how sparse/dense a network is-a property we described as "sparseness". Overall, structurally important nodes were found to have significant biological relevance.
3 citations
21 May 2014
TL;DR: In the context of an IP network, an interesting case of the inverse shortest path problem is investigated using the concept of network centrality, and a heuristic approach is proposed to obtain a centrality distribution that maximizes the entropy.
Abstract: In the context of an IP network, we investigate an interesting case of the inverse shortest path problem using the concept of network centrality. For a given network, the centrality distribution associated with the links of a network can be determined based on the number of shortest paths passing through each link. An entropy measure for this distribution is defined, and we then forumulate the inverse shortest problem in terms of maximizing this entropy. We then obtain a centrality distribution that is as broadly distributed as possible subject to the topology constraints. An appropriate change in the weight of a link alters the number of shortest paths that pass through it, thereby modifying the centrality distribution. The idea is to obtain a centrality distribution that maximizes the entropy. This problem is shown to be NP-hard, and a heuristic approach is proposed. An application to handling link failure scenarios in Open Shortest Path First routing is discussed.
3 citations
01 Aug 2015
TL;DR: A new centrality measure is proposed which is based not only on the nearest neighborhood of a node, but also on its 2-step and 3-step neighbors, which shows that the proposed centrality is a much more accurate measure to predict spreading capability of nodes in real-world networks.
Abstract: Identifying the most influential spreaders in a complex network is important in optimizing the use of available resource and controlling spreading behaviors on it. Centrality is usually used to measure the importance of a node within the network, such as degree, betweenness, closeness, eigenvector, k-core, etc. Here considering the local connection pattern of nodes in the network structure, we propose a new centrality measure which is based not only on the nearest neighborhood of a node, but also on its 2-step and 3-step neighbors. To evaluate its effectiveness, we use the classic spreading model to simulate the spreading efficiency of nodes in the network and compare the performance of the proposed centrality with the most widely used centrality of degree and coreness in ranking spreaders. Results show that the proposed centrality is a much more accurate measure to predict spreading capability of nodes in real-world networks.
3 citations