Topic
Network theory
About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.
Papers published on a yearly basis
Papers
More filters
01 Dec 2011
TL;DR: A review and synthesis of the studies that have examined the phenomenon of innovation from a social network perspective can be found in this paper, where the current state of the literature, gaps and future research direction are discussed.
Abstract: Innovation has been studied from several different perspectives. Since innovation has increasingly become a nonlinear, interactive and open activity, social network analysis provides a handy tool to examine this phenomenon. This paper aims to review and synthesize the studies that have examined the phenomenon of innovation from a social network perspective. The current state of the literature, gaps and future research direction are discussed.
6 citations
Posted Content•
TL;DR: This paper proposes a generic axiomatic framework to capture all the intrinsic properties of a centrality measure (a.k.a. centrality index), and analyze popular centrality measures along with other novel measures of centrality using this framework.
Abstract: Centrality is an important notion in complex networks; it could be used to characterize how influential a node or an edge is in the network. It plays an important role in several other network analysis tools including community detection. Even though there are a small number of axiomatic frameworks associated with this notion, the existing formalizations are not generic in nature. In this paper we propose a generic axiomatic framework to capture all the intrinsic properties of a centrality measure (a.k.a. centrality index). We analyze popular centrality measures along with other novel measures of centrality using this framework. We observed that none of the centrality measures considered satisfies all the axioms.
6 citations
TL;DR: In this paper, the authors investigate firms' centrality in the financial network as an explanatory variable in corporate failure prediction and also as a systemic risk measure, and they find that firms with high centrality are more likely to fail.
Abstract: A financial market can be expressed in a network structure where the stocks resides as nodes and the links account for returns correlation. Centrality measure in the financial network structure captures firms’ embeddedness and connectivity in the capital market structure. This paper investigates firms’ centrality in the financial network as an explanatory variable in corporate failure prediction and also as a systemic risk measure. First, when analyzing the CDS spreads, I find peripheral firms in the network to have higher average CDS spreads and higher propensity to CDS jump events. Second, centrality is found to increase the explanatory power of default prediction models and moreover, it is negatively related to failure and bankruptcy probability. This implies that peripheral firms in the network are more likely to fail. Finally, examining the out-of-sample performance of centrality as a systemic risk measure, I find that centrality distinguish correctly the firms that suffered a higher loss during the 2007/2008 crisis period.
6 citations
12 Apr 2017
TL;DR: A random sampling of frames method (MSF), based on a statistical approach, and an algorithm to estimate the occurrence of 3-motifs in networks with directed links is proposed and its significant advantages in terms of accuracy, speed and consumption of RAM are revealed.
Abstract: The task of development of efficient algorithms for estimating the frequency of occurrence of non-isomorphic connected subnets (motifs) on a given number of nodes is an important task of network theory. Combinatorial and logical nature of this problem makes the calculation time-consuming and/or causes high consumption of RAM when estimating networks with hundreds of thousands of nodes. In order to solve the problem this paper develops a random sampling of frames method (MSF), based on a statistical approach, and an algorithm to estimate the occurrence of 3-motifs in networks with directed links is proposed. We suggest implementing the algorithm with the help of parallel computing. The results of numerical data experiments are given. When comparing the developed algorithm with other known algorithms its significant advantages in terms of accuracy, speed and consumption of RAM are revealed in some cases.
6 citations
26 May 2015
TL;DR: The contribution of this paper is to select team members based on both closeness centrality and eigenvector centrality, and shows that this approach completes in lower time compared to the previous methods.
Abstract: In recent years, the growth and popularity of social networks have created a new world of collaboration and communication. Team formation is a new research topic in the area of social network analysis. Consider there is a social network of experts and the goal is to form the best possible team out of them for a given project. The best solution is a team with the minimized communication cost within team members. A social network is modeled as a graph, in which nodes represent experts and an edge between two nodes shows a prior collaboration of the two experts. The contribution of this paper is to select team members based on both closeness centrality and eigenvector centrality. Experimental results on the DBLP dataset show that our approach completes in lower time compared to the previous methods.
6 citations