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Bunke H., Dickinson P.J., Kraetzl M., Wallis W.D. A Graph-Theoretic Approach to Enterprise Network Dynamics Progress in Computer Science and Applied Logic

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Bunke H., Dickinson P.J., Kraetzl M., Wallis W.D. A Graph-Theoretic Approach to Enterprise Network Dynamics Progress in Computer Science and Applied Logic
Издательство Birkhäuser, 2007, -238 pp.
In this monograph, we describe the application of many graph-theoretic algorithms to a comprehensive environment of analysis of dynamic enterprise networks. Networks are ubiquitous, increasingly complex, and dynamic. Since they are part of all aspects of human life, their support of modern enterprise environments is paramount. Enterprises in general are becoming more information-based, and proper networking support depends on optimal performance management of all intranets involved in populating information and knowledge databases. Among other parameters, network dynamics analysis yields valuable information about network performance, efficiency, fault prediction, cost optimization, indicators, and warnings. After many years of applied research of generic network dynamics, we have decided to write a chronicle of our investigations to date with emphasis on enterprise networks. The motivation was two-fold: first, we wanted to convey to practitioners involved in network analysis a number of elegant applications of traditional graph-theoretic algorithms and techniques to computationally-tractable network dynamics analysis; second, we wanted to motivate researchers in other areas of mathematics, statistics, and computer science to apply similar reasoning in implementation of their approaches to analysis of dynamic enterprise networks. This monograph is also suitable for various graduate-level courses addressing state-of-the art applications of graph theory in analysis of dynamic communication networks, dynamic databasing, knowledge management, and many related applications of network dynamics.
Introduction
Intranets and Network Management
Graph-Theoretic Concepts
Event Detection Using Graph Distance
Matching Graphs with Unique Node Labels
Graph Similarity Measures for Abnormal Change Detection
Median Graphs for Abnormal Change Detection
Graph Clustering for Abnormal Change Detection
Graph Distance Measures based on Intragraph Clustering and Cluster Distance
Matching Sequences of Graphs
Properties of the Underlying Graphs
Distances, Clustering, and Small Worlds
Tournament Scoring
Prediction and Advanced Distance Measures
Recovery of Missing Information in Graph Sequences
Matching Hierarchical Graphs
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