Principles of Big Graph: In-depth Insight

Call for Book Chapter:

To be published in Advances in Computers, Elsevier (Indexed in: SCIE, Web of Science, Scopus etc.) [IF: 1.833]

Big Graph is one of the most recent emerging research fields which is gaining enormous popularity among academicians, industrialists, and practitioners. Also, Big Graph is applied in many research domains, for instance, Bioinformatics, Social networking, Computer Networking, Complex Networks, Data Streaming and many more. Big Graph has become an important research field due to ever growing data size. Conventional Graph databases and analytics are unable to solve the dilemma of scalability of graph data. Processing large scale graph data becomes expensive in terms of computation. Therefore, Big Graph plays a vital role in mining meaningful information from graph data. However, Big Graph analytics, mining and storing are also a big deal.

Nowadays, the World is interconnected through the Internet, for instance, social media. Not only social media relies on Big Graph technology but also biological networks, scholar article citation networks, protein protein interaction, and semantic networks rely on Big Graph. Therefore, Big Graph is able to influence the researchers, developers and practitioners. Since, these graphs consist of millions of nodes and trillion of edges. Hence, processing of these large graphs becomes a grand challenge. The Big Graphs are growing exponentially and it needs a large computing machinery.

Current scenario, there are several tools of Big Graph available in the marketplace. However, there is a single book available in the current marketplace which emphasizes analytics. The book is unable to provide rich insight on analytics, visualization and databases. Hence, our book is being planned to bring forth all the right information regarding Big Graph so the database professionals and professors across the world are supplied with the relevant expertise, education and experience.

 Submission Guidelines

Submission link
Abstract registration deadline:  Nil
Submission deadline: January 15, 2021 Hard deadline

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

  • A chapter cannot exceeds plagiarism of 10% excluding references.
  • A chapter cannot be submitted multiple places.
  • Length of the pages: 12-40 pages.
  • The chapter must be formatted as per the guideline of Advances in Computers, Elsevier, link:
  • Review process is single blind. Therefore, author names and affiliations must be included. Do not write professional title, like Dr., Prof., etc.

List of Topics

  • Big Data and Big Graph
  • NoSQL for Big Graph
  • Big Graph Architecture
  • Big Graph Mining and Analytics
  • Big Graph Visualization
  • Big Graph Applications
  • In-memory Big Graph
  • In-memory Big Graph Storage Architecture
  • In-memory Big Graph Frameworks
  • In-memory Big Graph databases
  • In-memory Big Graph Analytics
  • Big Graph Databases: SSD and HDD based Big Graph
  • Big Graph frameworks based on SSD
  • SSD-based Big Graph Databases
  • SSD-based Big Graph Analytics
  • Big Graph Framework based on HDD
  • HDD-based Big Graph Databases
  • HDD-based Big Graph Analytics
  • Big Graph Mining: Discoveries and Analytics
  • Big Graph processing frameworks
  • Pregel and Pregelix, GraphLab, Blogel, Pegasus, GraphX, Giraph, Mizan
  • GPS, Graph Sample and Hold
  • Knowledge Graph
  • Knowledge Graph Semantic
  • DBpedia (English), YAGO, Freebase, Wikidata, NELL, OpenCyc, Google’s Knowledge Graph, Google’s Knowledge Vault, Yahoo! Knowledge Graph,
  • Knowledge graph embedding,
  • Multidimensional Knowledge graph
  • Big Graph Visualization
  • Big Graph Visualization using Big Data Tools
  • Interactive Graph Analaytics
  • Visualization of Protein-protein Interaction
  • Social Network Visualization
  • Vertex-centric Computing
  • Bulk Synchronous Parallel
  • Think-like-a-vertex (TLAV)
  • Synchronous TLAV
  • Asynchronous TLAV
  • Hybrid TLAV
  • Trinity, PowerGraph, Giraph, Pregel, Hama, GRACE, PowerSwitch, GraphHP, P++
  • Algorithms on Big Graphs
  • Pagerank
  • Connected Component
  • Single source shortest path
  • Network Motifs
  • de Bruijn Graph
  • Facebook Friends
  • Twitter followers
  • Recommendation engines
  • Hadoop Framework for Big Graph
  • MapReduce codes for Graph Processing
  • Graph Processing on Spark
  • Giraph Framework
  • Web Graphs
  • Static and Dynamic Big Web Graphs
  • Big Graph Search
  • Big Web Graph Analytics
  • Centrality Measures on Big Graphs
  • Web of Linked Data
  • Visualization of Big web graph
  • DNA Sequencing
  • de novo Sequencing
  • Bloom Filter for Big Graph
  • k-mer counting
  • Read compression
  • Error Correction
  • Indexing
  • de Bruijn Graph
  • Weighted de Bruijn Graph
  • Colored de Bruijn Graph
  • Compressed de Bruijn Graph
  • DNA Sequencing Techniques
  • Postprocessing Filtering
  • Big Graph for Network Security
  • Cyber Attack Graphs
  • Complex Linkage Attack
  • Big Graph for privacy
  • Attack Graph Analysis
  • Optimal Attack Path Analysis
  • Anomaly detection in Social Networks
  • Big Graph for Computer Networking
  • Network Data Repository with Graph Analysis
  • Network traffic monitoring and analysis
  • Information flow monitoring
  • Network Visualization
  • Social Networks
  • Community detection
  • Social Network Visualization
  • Social Networks analysis in business intelligence
  • Social Network Analysis in healthcare
  • Sentiment Analysis using Big Graph
  • Socio-cyber crime
  • Network Motifs
  • Induced Subgraphs
  • Frequency
  • Application Areas of Network Motifs
  • Applications of Network Motif
  • Technique used to detect Network Motifs
  • Scholarly Article Citation Networks
  • Big Scholarly Data
  • Academic Social Networks
  • Scholarly Network Analysis
  • Citation Network Analysis
  • Co-author Networks
  • Co-Citation Networks
  • Bibliographic Coupling Networks
  • Scientific Impact Analysis
  • Big Streaming Graph
  • Big Graph streaming partitioning
  • Homogenous Environment
  • Heterogeneous environment
  • Streaming Data Analysis
  • Graph Stream Mining
  • Big Graph for Recommendation Engine
  • Personalized Recommendation
  • Friend recommendation
  • Twitter follower analysis
  • Discovery of Frequently Connected Friends
  • Product recommendation
  • Big Graph Search
  • Significance of Graph Search
  • Complex object identification
  • Pattern Search
  • Software plagiarism detection
  • Traffic route planning
  • Graph Queries
  • Graph Search Algorithms
  • Machine Learning for Big Graph
  • Big Graph Learning approach
  • SVM for Big Graph
  • Random Forest for Big Graph
  • Tensorflow for Big Graph
  • Extreme learning for Big Graph
  • Convolution Neural Networks for Big Graph



  • Dr. Ripon Patgiri, National Institute of Technology Silchar
  • Ganesh Chandra Deka, Deputy Director, International Cooperation & Technology, Ministry
    of Skill Development and Entrepreneurship, Govt. of India
    New Delhi, INDIA
  • Dr. Anupam Biswas, National Institute of Technology Silchar



The book, “Principles of Big Graph: In-depth Insight”, will be published in “Advances in Computers“, Elsevier which is indexed in SCIE, Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and ScienceDirect.


All questions about submissions should be emailed to, and