Academy & Industry Research Collaboration Center (AIRCC)

Graph Theoretic Approaches for Analyzing Large-Scale Social Networks
Edited by, Natarajan Meghanathan

To be published by: IGI Publishers, USA

Call for Book Chapters



Social Network Analysis is a rapidly emerging area within the discipline of Data Science and is of immense interest for both theory and practice. Social Network Analysis is typically conducted by modeling the social network as a directed or undirected graph of nodes and edges. With the phenomenal growth of social networks as well as the Internet and web, it is imperative that we need algorithms to analyze such large-scale networks modeled as graphs and extract useful information (like communities in the networks, information diffusion, critical nodes for network robustness, etc). Social Network Analysis involves the analysis and visualization of large-scale complex real-world network graphs and the development of efficient algorithms to study networks comprising hundreds and thousands of nodes.

Social Network Analysis falls within the realm of "Big Data Computing" where the Big Data is the large-scale graphs that model complex real-world social networks. The proposed book will include chapters presenting research advances in graph theory-based algorithms and solution approaches for analyzing large-scale social networks. The book will present the applications of the theoretical algorithms and graph models to analyze real-world large-scale social networks and the data emanating from them as well as characterize the topology and behavior of these networks. The book will also explore the use of advanced graph theoretic approaches for paradigms like centrality metrics, community detection, anomaly detection, diffusion, behavior and identity detection, link and node prediction, etc with respect to the analysis of large-scale social networks.


The overall objective of the proposed book is to bring together the recent research advances in the field of graph theory for analyzing large-scale social networks. We propose to bring together the research advances in state-of-the-art graph theory algorithms and techniques that have contributed to the effective analysis of social networks, especially those networks that generate significant amount of data and involve several hundreds and thousands of users. To accomplish the above objective, we plan to have each chapter discussing one or more novel graph theoretic research applied for large-scale social networks and/or the volume of data generating from them. Thus, the mission of this book is to treat graph theory as an integral part of social network analysis. In this pursuit, we seek to simultaneously explore the application of research advances in graph theory and solution techniques that could be used for analyzing large-scale social networks as well as explore open research problems in social network analysis that have lead to the development of advanced graph theoretic algorithms.

The book will serve as an ideal example to illustrate the practical applications of theoretical research. The chapters collected for the book will present how graph theory research can be conducted in conjunction with research in social network analysis and demonstrate the potential benefits of research efforts in analyzing large-scale graphs for the Big Data community. We expect the analytical approaches proposed in these chapters to be of potential use for integration with the current generation of social networks as well as with the open source tools that are available for analyzing social networks. We anticipate the capability of these tools to analyze large-scale social networks to be significantly improved with the incorporation of the algorithms and methodologies proposed in our chapters to analyze network graphs comprising of several hundreds and thousands of users.

Prospective Topics of Interest

We encourage prospective authors to submit book chapters on one or more of the following topics related to ad hoc networks and their variants. Chapters on any other related topic in this area are also welcome. 
1. Graph Models for Analyzing Social Network Data  
2. Community Detection Algorithms  
3. Centrality Metrics and Algorithms 
4. Anomaly and Outlier Detection Algorithms 
5. Diffusion Algorithms for Social Networks 
6. Search Algorithms for Social Networks
7. Algorithms for Recommendation Networks
8. Link and Node Prediction Algorithms
9. Egocentric Approaches for Social Network Analysis
10.Contextual Analysis for Social Networks
11. Simulation and Analysis of Models for Emulating Social Networks  
12. Tradeoffs among Metrics for Social Network Analysis  
13. Behavior and Identity Detection and Monitoring
14. Exponential Random Graph Models
15. Spectral Analysis of Social Networks
16. Stochastic Actor-based Graph Models
17. Graph Models for Core/Periphery Structures

Submission Procedure

Researchers and practitioners are invited to submit on or before June 15, 2016, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 30, 2016 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by October 15, 2016, and all interested authors must consult the guidelines for manuscript submissions at prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the E-Editorial DiscoveryTM online submission manager.

Target Audience

we anticipate our proposed book to serve as a good source of reference for both students and faculty pursuing research in social network analysis. As a majority of the books in social network analysis are textbook-oriented and focus on using only the traditional graph theoretic measures for analyzing social networks, the research-oriented chapters in our proposed book will serve as a good source for students and faculty to identify open research problems and the state-of-the-art graph theory-based solution techniques that have been introduced to solve these problems. We are very confident that the proposed book will motivate graduate students to explore open research problems in graph theory that if solved would lead to further advances in social network analysis, especially in the context of analyzing large-scale social networks and the data emanating from these networks. In this context, we expect the proposed book to serve as a recommended book for graduate-level courses in the Network Science and Data Science areas as it could potentially aid the graduate students to identify research problems that they could work on for their theses and dissertations. 

Important Dates

Chapter Proposals Due August 15, 2016
Full chapters Due October 15, 2016
Review Results sent to Authors December 30, 2016
Revised Chapters Due from Authors January 30, 2017
Final Acceptance Notification sent to Authors February 28, 2017
Submission of Final Formatted Chapters to Editor March 30, 2017


All submissions (proposals, full book chapters and any inquiries) should be sent to by the deadlines mentioned above.

Chief Editor Contact Information

Dr. Natarajan Meghanathan
Associate Professor
Department of Computer Science
Jackson State University, MS, USA