Network theory

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

Graph theoretic or computational geometric research of cellular mobile communications

Abstract: The demand fur communication services is rapidly increasing, because the mobile communication service is synonymous of an ideal communication style realizing communication in any time, any where and with anyone There exist various problems to which computational geometry and graph and network theory is applicable in mobile communication services For example, it is well known that coloring algorithms of graphs are applicable in channel assignment algorithms in cellular systems In this paper, we show new results in graph theoretic or computational geometric research of cellular mobile communications

Use and Methods of Social Network Analysis in Knowledge Management

Abstract: Whilst the primary importance of informal communities of practice and knowledge networks in innovation and knowledge management is widely accepted (see Armbrecht et al., 2001; Brown & Duguid, 1991; Collinson & Gregson, 2003; Jain & Triandis, 1990; Lesser, 2001; Liyanage, Greenfied & Don, 1999; Nahapiet & Ghoshal, 1998; Nohria & Eccles, 1992; Wenger, 1999; Zanfei, 2000), there is less agreement on the most appropriate method for their empirical study and theoretical analysis. In this article it is argued that social network analysis (SNA) is a highly effective tool for the analysis of knowledge networks, as well as for the identification and implementation of practical methods in knowledge management and innovation. Social network analysis is a sociological method to undertake empirical analysis of the structural patterns of social relationships in networks (see, e.g., Scott, 1991; Wasserman & Faust, 1994; Wellman & Berkowitz, 1988). This article aims at demonstrating how it can be used to identify, visualize, and analyze the informal personal networks that exist within and between organizations according to structure, content, and context of knowledge flows. It will explore the benefits of social network analysis as a strategic tool on the example of expert localization and knowledge transfer, and also point to the limits of the method.

Unified theory on topological analysis of linear systems

Abstract: Topological formulas were stated by Kirchhoff for mesh equations, by Maxwell for nodal equations, by Mason for signal-flow graphs and by Coates for flow graphs. In the paper, it is shown that all these formulas can be obtained from one another. Using these relationships, a generalised star-mesh transformation in network theory is obtained. Simple ways of drawing these associated directed graphs from a given network and illustrative examples are also given.

Playing with networks: how economists explain

Abstract: Network theory is applied across the sciences to study phenomena as diverse as the spread of SARS, the topology of the cell, the structure of the Internet and job search behaviour. Underlying the study of networks is graph theory. Whether the graph represents a network of neurons, cells, friends or firms, it displays features that exclusively depend on the mathematical properties of the graph itself. However, the way in which graph theory is implemented to the modelling of networks differs significantly across scientific fields. This article compares the economics variant of network theory with those of other fields. It shows how the methodology employed by economists to model networks is shaped by two explanatory desiderata: that the explanandum phenomenon is based on micro-economic foundations and that the explanation is general.

Performance analysis of an algorithm for computation of betweenness centrality

Abstract: In social network analysis, graph-theoretic perceptions are used to realize and explain social experience. Centrality indices are essential in the analysis of social networks, but are costly to compute. An efficient algorithm for the computation of betweenness centrality is given by Brandes that has time complexity O(nm + n2logn) and O(n + m) space complexity, where n, m are the number of vertices and edges in a graph, respectively. Some social network graphs are invariably huge and dense. Moreover, size of memory is rapidly increasing and the cost of memory is decreasing day by day. Under these circumstances, we investigate how the computation of centrality measures can be done efficiently when space is not very significant. In this paper, we introduce a time efficient and scalable algorithm for the accurate computation of betweenness centrality. We have made a thorough analysis of our algorithm visa-vis Brandes' algorithm. Experimental results show that our algorithm has a better performance with respect to time, but at the expense of using higher memory. Further performance improvement of our algorithm has been achieved by implementing it on parallel architectures.