Network theory

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

Estimating complex networks centrality via neural networks and machine learning

Abstract: Vertex centrality measures are important analysis elements in complex networks and systems. These metrics have high space and time complexity, which is a severe problem in applications that typically involve large networks. To apply such high complexity metrics in large networks we trained and tested off-the-shelf machine learning algorithms on several generated networks using five well-known complex network models. Our main hypothesis is that if one uses low complexity metrics as inputs to train the algorithms, one will achieve good approximations of high complexity measures. Our results show that the regression output of the machine learning algorithms applied in our experiments successfully approximate the real metric values and are a robust alternative in real world applications, in particular in complex and social network analysis.

Game-theoretic Network Centrality: A Review

Abstract: Game-theoretic centrality is a flexible and sophisticated approach to identify the most important nodes in a network. It builds upon the methods from cooperative game theory and network theory. The key idea is to treat nodes as players in a cooperative game, where the value of each coalition is determined by certain graph-theoretic properties. Using solution concepts from cooperative game theory, it is then possible to measure how responsible each node is for the worth of the network. The literature on the topic is already quite large, and is scattered among game-theoretic and computer science venues. We review the main game-theoretic network centrality measures from both bodies of literature and organize them into two categories: those that are more focused on the connectivity of nodes, and those that are more focused on the synergies achieved by nodes in groups. We present and explain each centrality, with a focus on algorithms and complexity.

Systems, Network, and Culture

Abstract: The paper compares social systems theory and social network theory in terms of what it is they respectively seek to elucidate. Whereas systems theory focuses on problems of difference and reproduction, network theory deals with problems of identity and control, the former privileging communication and the latter action. To understand their different foci, it may help to keep in mind that systems theory is a child of computing's formative years, whereas the more recent success of network theory, despite its roots in a far older tradition, accompanies the advent of the Internet. The paper goes on to compare the two theories with respect to questions of mathematical modeling, culture, and self-reference, which interestingly are closely related. It concludes by referring to Bronislaw Malinowski's 'scientific theory of culture' to propose a mathematical modeling of culture, which uses George Spencer-Brown's notion of form to combine variables of communication, consciousness, and life into one network relying on three systems capable of reproducing themselves.

Application of network methods for understanding evolutionary dynamics in discrete habitats.

Abstract: In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.

On the limiting behavior of parameter-dependent network centrality measures

Abstract: We consider a broad class of walk-based, parameterized node centrality measures for network analysis. These measures are expressed in terms of functions of the adjacency matrix and generalize various well-known centrality indices, including Katz and subgraph centrality. We show that the parameter can be "tuned" to interpolate between degree and eigenvector centrality, which appear as limiting cases. Our analysis helps explain certain correlations often observed between the rankings obtained using different centrality measures, and provides some guidance for the tuning of parameters. We also highlight the roles played by the spectral gap of the adjacency matrix and by the number of triangles in the network. Our analysis covers both undirected and directed networks, including weighted ones. A brief discussion of PageRank is also given.