Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information.

Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science.

The many academic areas covered in this publication include, but are not limited to:

Content Specific Modeling
Distributed Memory
Graph Mining
Influence Maximization
Information Spread Control
Link Prediction
Probabilistic Exploration