Application Oriented Analysis of Large Scale Datasets

Diverse application areas, such as social network, epidemiology, and software engineering consist of systems of objects and their relationships. Such systems are generally modeled as graphs. Graphs consist of vertices that represent the objects, and edges that represent the relationships between them. These systems are data intensive and it is important to correctly analyze the data to obtain meaningful information. Combinatorial metrics can provide useful insights for analyzing these systems. In this thesis, we use the graph based metrics such as betweenness centrality, clustering coefficient, articulation points, etc. for analyzing instances of large change in evolving networks (Software Engineering), and identifying points of similarity (Gene Expression Data). Computations of combinatorial properties are expensive and most real world networks are not static. As the network evolves these properties have to be recomputed. In the last part of thesis, we develop a fast algorithm that avoids redundant recomputations of communities in dynamic networks

Recent Advances in Fully Dynamic Graph Algorithms

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms