Over the last decade, we have witnessed how social networks have evolved from being an entertaining extra to an integrated part of nearly every aspect of peoples’ daily lives. Social networks have profoundly changed how we interact with the world around us, including the ways to access news and information, the strategies to run business, the policy guidelines to prevent virus pandemic, the response to deal with disasters, the communication channels to improve healthcare and public health, etc.
At the same time, widespread usage of social networks has introduced various security and privacy challenges.
The arrival of smart mobile devices and the booming of mobile social applications in the recent years have only accelerated this trend. The shipment of social media users in January 2020 was about 3.80 billion, with an increase rate of 7 percent per year. Social networks naturally generate an unprecedented volume of graph data continuously, which pave a road for designing high quality services and applications such as recommendation systems, event detection, scam detection, rumor blocking, and privacy leakage detection, taking advantage of powerful machine learning techniques and tools.
The existing graph-powered learning methods cannot effectively capture and process sequential, topological, geometric, or other relational characteristics of graphical data, which is one of the major barriers to the widespread adoption of social network-based applications. Furthermore, these continuously evolving networks pose several challenges like growing user population, heterogeneity of user activities, explosion of generated data, and increasing concern of privacy leakage. Thus, there is an unprecedented need for more advanced graph-powered learning methods to be scalable for large-scale networks, feasible for utilizing multimodal data, flexible to model complex patterns, and capable of protecting user privacy. The goal of the special issue is to solicit high-quality, high-impact, original papers aiming at investigating emerging techniques and trending applications under the social network scenario using sophisticated graph- power learning methods. We are interested in submissions covering different types of models for sustainable services and applications in social networks.
Topics of Interest
Topics of particular interest include, but are not limited to:
Graph representation learning in social networks
Graph-based methods for analyzing social networks
Graph-based methods on anomaly detection for social networks
Graph classification, clustering, link prediction for social networks
Graph-based methods on recommendation for social networks
Knowledge graph modeling and management for social networks
Graph-based methods on privacy enhancing and anonymization techniques in social networks
Acceleration for graph-based methods in social networks
Parallel and distributed algorithms for graph data in social networks
Visualization of graph data in social networks
Graphical data based multimodal data fusion for social networks
Large scale graphical data processing for social networks
Graph-based methods for real/industry applications and systems for social networks
Submission of manuscript: April 15, 2021
First-round peer review: June 30, 2021
Revised version due: August 01, 2021
Final notifications: September 15, 2021
The website link of Neurocomputing is https://www.sciencedirect.com/journal/neurocomputing and before submission, authors should carefully read the journal’s Author Guidelines, which are located at https://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors. When submitting your manuscript please select the article type “VSI: Graph Learning Social”. Please submit your manuscript before the submission deadline.
All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Please see an example here: https://www.sciencedirect.com/journal/science-of-the-total-environment/special-issue/10SWS2W7VVV
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage.
Zhipeng Cai, Georgia State University, USA
Christian Esposito, University of Salerno, Italy
Tooska Dargahi, University of Salford, Manchester, UK
Chaokun Wang, Tsinghua University, China