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.

Networks have become nearly ubiquitous and increasingly complex, and their support of modern enterprise environments has become fundamental. Accordingly, robust network management techniques are essential to ensure optimal performance of these networks. This monograph treats the application of numerous graph-theoretic algorithms to a comprehensive analysis of dynamic enterprise networks. Network dynamics analysis yields valuable information about network performance, efficiency, fault prediction, cost optimization, indicators and warnings. The exposition is organized into four relatively independent parts: an introduction and overview of typical enterprise networks and the graph theoretical prerequisites for all algorithms introduced later; an in-depth treatise of usage of various graph distances for event detection; a detailed exploration of properties of underlying graphs with modeling applications; and a theoretical and applied treatment of network behavior inferencing and forecasting using sequences of graphs. Based on many years of applied research on generic network dynamics, this work covers a number of elegant applications (including many new and experimental results) of traditional graph theory algorithms and techniques to computationally tractable network dynamics analysis to motivate network analysts, practitioners and researchers alike. The material is also suitable for graduate courses addressing state-of-the-art applications of graph theory in analysis of dynamic communication networks, dynamic databasing, and knowledge management.