In this article, we investigate several issues related to the use of the index S G = d i d j , known as the Zagreb index (see Gutman and Das, 2004) or "Smetric" (Alderson and Li, 2007). We present some new upper and lower bounds for S G , in terms of the degree sequence of G. Then, we concentrate on trees and prove that in trees with maximum S G the eigenvector ordering is coherent with the degree ordering; that is, degree central vertices are also eigenvector central. This confirms results given in Bonacich (2007). Further, we show that these trees have minimum diameter and maximum spectral radius in the set of trees with a given degree sequence. A simple application to a company organizational network is provided.
There is convergent consensus among scientists that many social, economic and financial phenomena can be described by a network of agents and their interactions. Surprisingly, even though the application fields are quite different, those networks often show a common behaviour. Thus, their topological properties can give useful insights on how the network is structured, which are the most "important" nodes/agents, how the network reacts to new arrivals. Moreover the network, once included into a dynamic context, helps to model many phenomena. Among the topics in which topology and dynamics are the essential tools, we will focus on the diffusion of technologies and fads, the rise of industrial districts, the evolution of financial markets, cooperation and competition, information flows, centrality and prestige.The volume, including recent contributions to the field of network modelling, is based on the communications presented at NET 2006 (Verbania, Italy) and NET 2007 (Urbino, Italy); offers a wide range of recent advances, both theoretical and methodological, that will interest academics as well as practitioners.Theory and applications are nicely integrated: theoretical papers deal with graph theory, game theory, coalitions, dynamics, consumer behavior, segregation models and new contributions to the above mentioned area. The applications cover a wide range: airline transportation, financial markets, work team organization, labour and credit market.The volume can be used as a reference book for graduate and postgraduate courses on Network Theory and Complex Systems in Faculties of Economics, Mathematics, Engineering and Social Sciences. In Part I, the invited tutorials introduce Graph Theory from the theoretical point of view (Marusic) and the possible applications to economics (Battiston). In Part II, the contributions cover local and global interaction, complex behavior, network games, while in Part III they refer to Markov chains and topology. The applications are all placed in Part IV.Fifteen papers have been selected among roughly thirty submitted extended abstracts; each paper has been reviewed by two referees. Space limitations are the main reason why no more papers have been accepted, although many of them were really interesting. v vi Preface
Assortativity was first introduced by Newman and has been extensively studied and applied to many real world networked systems since then. Assortativity is a graph metrics and describes the tendency of high degree nodes to be directly connected to high degree nodes and low degree nodes to low degree nodes. It can be interpreted as a first order measure of the connection between nodes, i.e. the first autocorrelation of the degree-degree vector. Even though assortativity has been used so extensively, to the author's knowledge, no attempt has been made to extend it theoretically. This is the scope of our paper. We will introduce higher order assortativity by extending the Newman index based on a suitable choice of the matrix driving the connections. Higher order assortativity will be defined for paths, shortest paths, random walks of a given time length, connecting any couple of nodes. The Newman assortativity is achieved for each of these measures when the matrix is the adjacency matrix, or, in other words, the correlation is of order 1. Our higher order assortativity indexes can be used for describing a variety of real networks, help discriminating networks having the same Newman index and may reveal new topological network features.Comment: 24 pages, 16 figure
In this paper we discuss the role of centrality in organizational networks. We will present some new results related to the different concepts of centrality. A case study of an ICT consulting company concludes.
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