Graph Mining: Practical Uses and Instruments for Exploring Complex Networks (Synthesis Lectures on Computer Science) - Tapa dura

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Riju Bhattacharya, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at GITAM Deemed to be University in India.  He completed his Ph.D. at the National Institute of Technology, Raipur.  Previously he worked as an associate professor and head of the Department of Information Technology at Shri Shankaracharya Institute of Professional Management and Technology, Raipur affiliated to CSVTU, Bhilai.  Dr. Bhattacharya’s research interests include social network analysis, graph mining, link prediction, and data science and is involved in many community initiatives in the areas of machine learning, deep learning, and data science.

Yogesh Kumar Rathore, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at Shri Shankaracharya Institute of Professional Management and Technology in        Raipur, India.  He completed his Ph.D. in Information Technology from the National Institute of Technology, Raipur.  Before that, he completed his M. Tech. from Chhattisgarh Swami Vivekanand Technical University and his B. Eng. from Pt. Ravishankar Shukla University. He has published two edited books, contributed chapters to internationally edited books, and authored a textbook on data mining.  Dr. Rathore has also been involved in patenting with three Indian patents published and two USA-granted patents, showcasing his expertise in the field of computer science engineering.

Tien Anh Tran, Ph.D., is a Senior Researcher at Seoul National University, Seoul City, South Korea and an Assistant Professor in the Department of Marine Engineering at the Vietnam Maritime University in Haiphong, Vietnam. He is an Honorary Professor at the School of Computing Science and Engineering at Galgotias University, India and an Honorary Adjunct Professor in the School of Computer Science and Engineering at Lovely Professional University (LPU), India. He received his B. Eng. and M.Sc at Vietnam Maritime University and his Ph.D. at Wuhan University of Technology, Wuhan, China. He is an Editor/Guest Editor for the reputation journals indexed in SCI/SCIE was one of five outstanding scientists in Vietnam to be nominated for the Ta Quang Buu prize by the National Foundation for Science and Technology Development (NAFOSTED).

Suman Kumar Swarnkar, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at Shri Shankaracharya Institute of Professional Management and Technology in        Raipur, India. He received his Ph.D. (CSE) from Kalinga University, Nayaraipur and his M.Tech. (CSE) from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He has contributed to book chapters published by Elsevier and Springer and is a member of IEEE, IAENG, ASR, IFERP, ICSES, Internet Society, UACEE, IAOP, IAOIP, EAI, and CSTA.  Dr. Swanrkar’s research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing. 

De la contraportada

This book provides a thorough introduction to graph mining and addresses foundational concepts and advanced techniques along with practical applications across various fields.  As graphs have become increasingly vital for data representation in domains such as social network analysis, bioinformatics, and transportation, there is a growing demand for a comprehensive source that covers both theory and practical insights. This book seeks to fill that gap by offering clear explanations, practical examples, and actionable insights, making complex graph mining techniques accessible to students, postgraduate readers, and researchers.  The authors also provide an extensive investigation into the process of gaining insightful knowledge from graph representations, ranging from interpreting intricate relationships to decoding complex data structures.  Readers will learn to identify anomalous patterns, locate communities, arrange nodes, predict connections, and evaluate graphs effectively.  

In addition, this book:

• Offers a concise road map for overcoming the challenges associated with graph-based data analysis
• Explains complex graph mining principles and methods in a clear, accessible manner
• Introduces cutting-edge trends in graph mining and presents practical applications and real-world case studies