Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

The Parameterized Complexity of Centrality Improvement in Networks

Abstract: The centrality of a vertex v in a network intuitively captures how important v is for communication in the network. The task of improving the centrality of a vertex has many applications, as a higher centrality often implies a larger impact on the network or less transportation or administration cost. In this work we study the parameterized complexity of the NP-complete problems Closeness Improvement and Betweenness Improvement in which we ask to improve a given vertex’ closeness or betweenness centrality by a given amount through adding a given number of edges to the network. Herein, the closeness of a vertex v sums the multiplicative inverses of distances of other vertices to v and the betweenness sums for each pair of vertices the fraction of shortest paths going through v. Unfortunately, for the natural parameter “number of edges to add” we obtain hardness results, even in rather restricted cases. On the positive side, we also give an island of tractability for the parameter measuring the vertex deletion distance to cluster graphs.

Emergence of the EU corporate lending network

Abstract: DISCLAIMER: This presentation should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB

Dynamic visualization of large financial networks

Abstract: This paper shows how large data sets can be visualized in a dynamic way to support data exploration, highlight econometric results or provide early warning information. We use payments and unsecured money market transaction data from the Dutch part of the Eurosystem's large value payment system, TARGET2, to showcase how video animations facilitate analysis at three different levels. First, animation shows how the market macrostructure develops. Second, it enables us to follow individual banks that are of interest. Finally, it facilitates a comparison of the same market at different times, and of different markets (such as countries) at the same time.

Modularity matrix of networks and the measure of community structure

Abstract: The relationships between positive （negative） eigenspectrums and the structure properties of community （anti-community） of complex networks are investigated, and some corresponding definitions are given. By using the multieigenspectrums of modularity matrix of networks, a kind of structural centrality measure called the community centrality, is introduced. This individual centrality measure describes the strength that the individual adheres to the corresponding community. The measure is illustrated and compared with the standard centrality measures using several artificial networks and real world networks data. The results show that the community centrality has better discrimination, and it has positive correlation with the degree centrality.

Entropy of a network ensemble: definitions and applications to genomic data.

Abstract: In this paper we introduce the framework for the application of statistical mechanics to network theory, with a particular emphasis to the concept of entropy of network ensembles. This formalism provides novel observables and insights for the analysis of high-throughput transcriptomics data, integrated with apriori biological knowledge, embedded in-to available public databases of protein-protein interaction and cell signaling.