Network theory

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

Network-Based Biomedical Data Analysis.

Abstract: Complex diseases are caused by disorders of both internal and external factors, and they account for a large proportion of human diseases. They are multigenetic and rarely a consequence of the dysfunction of single molecules. Systems biology views the living organism as an organic network. Compared with reductionism-based viewpoints, systems biology pays more attention to the interactions among molecules located at different omics levels. Based on this theory, the concepts of network biomarkers and network medicine have been proposed sequentially, which integrate clinical data with knowledge of network sciences, thereby promoting the investigation of disease pathogenesis in the era of biomedical informatics. The former aims to identify precise signals for disease diagnosis and prognosis, whereas the latter focuses on developing effective therapeutic strategies for specific patient cohorts. In this chapter, the basic concepts of systems biology and network theory are presented, and clinical applications of biomolecular networks, network biomarkers, and network medicine are then discussed.

Theoretical Tools for Network Analysis: Game Theory, Graph Centrality, and Statistical Inference.

Abstract: Theoretical tools for network analysis: Game theory, graph centrality, and statistical inference by Travis Bennett Martin Chairs: Mark E. Newman and Michael P. Wellman A computer-driven data explosion has made the di culty of interpreting large data sets of interconnected entities ever more salient. My work focuses on theoretical tools for summarizing, analyzing, and understanding network data sets, or data sets of things and their pairwise connections. I address four network science issues, improving our ability to analyze networks from a variety of domains. I first show that the sophistication of game-theoretic agent decision making can crucially e↵ect network cascades: di↵ering decision making assumptions can lead to dramatically di↵erent cascade outcomes. This highlights the importance of diligence when making assumptions about agent behavior on networks and in general. I next analytically demonstrate a significant irregularity in the popular eigenvector centrality, and propose a new spectral centrality measure, nonbacktracking centrality, showing that it avoids this irregularity. This tool contributes a more robust way of ranking nodes, as well as an additional mathematical understanding of the e↵ects of network localization. I next give a new model for uncertain networks, networks in

Vulnerability of C2 Networks to Attack: Measuring the Topology of Eleven Dutch Army C2 Systems

Abstract: : Effective Command and Control (C2) depends on a reliable networking infrastructure. Under current Royal Netherlands Army doctrine, C2 networks are designed to provide the connectivity, bandwidth, and low latency needed for military operations. Additionally, best practice provides redundancy against hardware and software failures. It is implicitly assumed that this redundancy also protects against the effects of enemy action. A recent development in mathematical network theory is the investigation of network resilience. Research shows that, depending on the topology, network robustness can differ greatly according to the way in which nodes or arcs are removed. In particular, scale-free networks are robust when nodes are removed randomly, but are vulnerable to targeted attack. To apply these results to the military domain, we need to measure the topology of existing C2 networks. In the 12th ICCRTS, Grant et al (2007) speculated that C2 networks, like the Internet and the World Wide Web, are scale-free networks. The purpose of this paper is to report the results of measuring the topology of eleven Royal Netherlands Army C2 systems, modeled as networks. These measurements confirm our speculation, with modeling guidelines emerging as a by-product of the research. We discuss the implications and make recommendations for doctrine and for further research. The presentation includes briefing charts.

WebRank: a hybrid page scoring approach based on social network analysis

Abstract: Applying the centrality measures from social network analysis to score web pages may well represent the essential role of pages and distribute their authorities in a web social network with complex link structures. To effectively score the pages, we propose a hybrid page scoring algorithm, called WebRank, based on the PageRank algorithm and three centrality measures including degree, betweenness, and closeness. The basis idea of WebRank is that: (1) use PageRank to accurately rank pages, and (2) apply centrality measures to compute the importance of pages in web social networks. In order to evaluate the performance of WebRank, we develop a web social network analysis system which can partition web pages into distinct groups and score them in an effective fashion. Experiments conducted on real data show that WebRank is effective at scoring web pages with less time deficiency than centrality measures based social network analysis algorithm and PageRank.