This book covers the content, keeping in mind the CS, IT and SS academic curricula for undergraduate and fresh graduate students. The coverage of the book has the right balance of the mathematical foundations and technological aspects of social networks, along with several applications of mining social network data. This book perhaps is the first of its kind that succinctly covers a set of critical fundamental concepts (theory) and gives detailed coverage of recent advances (practice). Additional online material (source codes, datasets, blogs, exercises, etc.), accompanying the chapters, along with quizzes and data challenges will strengthen the theoretical and practical skills of the students and professionals. More details of these resources and the book can be found at social-network-analysis.

The presence of social networks in our life cannot be ignored. We use social networks for almost all purposes in our daily lives — selecting a movie, visiting a tourist place, deciding who to vote in the election, criticising a government policy, and whatnot. The social network is often considered as the reflection of our society. Therefore, understanding the dynamics of social networks is of utmost importance. Social Network Analysis (SNA) has emerged as a regular elective course in today's academic curriculum. Being an interdisciplinary course, SNA cuts across and advances various fields, including computer science, statistical physics, social science, and mathematics. A newcomer may first need to go through these fundamental courses' basics and then start exploring SNA. This book is perhaps the first textbook written comprehensively for academic purposes, a one-in-all resource for a newcomer. The book reflects the author's 11 years of research experience in the area of social network analysis. The book maintains a nice balance of the theoretical aspects and the technological development in SNA, right from the historical concepts borrowed from classical statistical physics to the recent advances in deep learning. Every chapter is accompanied by a comprehensive exercise and a list of resources. The book also offers the readers to appear for three online quizzes and semester-wise hackathons or data challenges. The accompanying teaching materials are substantial for classroom teaching. The content of the book is designed for senior undergraduate students, fresh postgraduate students and IT professionals.