Advances in Computers: Principles of Big Graph: In-depth Insight, Volume One Hundred Twenty Eight, First Edition

  • 7h 17m
  • Anupam Biswas, Ganesh Chandra Deka, Ripon Patgiri
  • Elsevier Science and Technology Books, Inc.
  • 2023

Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review.

Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph.

  • Provides an update on the issues and challenges faced by current researchers
  • Updates on future research agendas
  • Includes advanced topics for intensive research for researchers

About the Author

Dr. Ripon Patgiri is an Assistant Professor at the Department of Computer Science & Engineering, National Institute of Technology Silchar, since 2013. His research interests include bloom filters, storage systems, security, and cryptography computing. He has published numerous papers in reputed journals, conferences, and books. Also, he has been awarded with several international patents. He is a senior member of IEEE. He was the General Chair of ICACNI 2018 and BigDML 2019. He is the Organizing Chair of FRSM 2020 and ADCOM 2020. Also, he is the Program Chair of CoMSO 2020, CoMSO 2021, and CoMSO 2022. He is also an editor of several multi-authored books. Moreover, he has received two research project fundings from SERB and DST, India.

Ganesh Chandra Deka is currently Deputy Director (Training) at Directorate General of Training, Ministry of Skill Development and Entrepreneurship, Government of India, New Delhi-110001, India. His research interests include e-Governance, Big Data Analytics, NoSQL Databases and Vocational Education and Training. He has 2 books on Cloud Computing published by LAP Lambert, Germany. He is the Co-author for 4 text books on Fundamentals of Computer Science (3 books published by Moni Manik Prakashan, Guwahati, Assam, India and 1 IGI Global, USA). As of now he has edited 14 books (6 IGI Global, USA, 5 CRC Press, USA, 2 Elsevier & 1 Springer) on Big data, NoSQL and Cloud Computing and authored 10 Book Chapters. He has published around 47 research papers in various IEEE conferences. He has organized 08 IEEE International Conferences as Technical Chair in India. He is the Member of the editorial board and reviewer for various Journals and International conferences. Member of IEEE, the Institution of Electronics and Telecommunication Engineers, India and Associate Member, the Institution of Engineers, India

Assistant Professor Anupam Biswas works in Computer Science and Engineering at the National Institute of Technology Silchar, Silchar, Assam, India.

In this Book

  • CESDAM: Centered Subgraph Data Matrix for Large Graph Representation
  • Bivariate, Cluster, and Suitability Analysis of NoSQL Solutions for Big Graph Applications
  • An Empirical Investigation on BigGraph using Deep Learning
  • Analyzing Correlation between Quality and Accuracy of Graph Clustering
  • geneBF: Filtering Protein-Coded Gene Graph Data using Bloom Filter
  • Processing Large Graphs with an Alternative Representation
  • MapReduce based Convolutional Graph Neural Networks: A Comprehensive Review
  • Fast Exact Triangle Counting in Large Graphs using SIMD Acceleration
  • A Comprehensive Investigation on Attack Graphs
  • Qubit Representation of a Binary Tree and Its Operations in Quantum Computation
  • Modified ML-KNN: Role of Similarity Measures and Nearest Neighbor Configuration in Multi-Label Text Classification on Big Social Network Graph Data
  • Big Graph Based Online Learning Through Social Networks
  • Community Detection in Large-Scale Real-World Networks
  • Power Rank: An Interactive Web Page Ranking Algorithm
  • GA-Based Energy Efficient Modeling of a Wireless Sensor Network
  • The Major Challenges of Big Graph and Their Solutions: A Review
  • An Investigation on Socio-Cyber Crime Graph
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