正文
Dr. Zhigang Zeng, Huazhong University of Science and Technology, Wuhan, Hubei, China,
[email protected]
Dr. Yaochu Jin, University of Surrey, Guildford, Surrey, United Kingdom,
[email protected]
4. Submission Instructions
Authors should prepare their manuscripts according to the "Instructions for Authors" guidelines of “Neurocomputing” outlined at the journal website https://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors. All papers will be peer-reviewed following a regular reviewing procedure. Each submission should clearly demonstrate evidence of benefits to society or large communities. Originality and impact on society, in combination with a media-related focus and innovative technical aspects of the proposed solutions will be the major evaluation criteria.
人工智能
Neurocomputing
Graph-Powered Learning for Social Networks
全文截稿: 2021-04-15
影响因子: 4.072
CCF分类: C类
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 3区
网址：
http://www.journals.elsevier.com/neurocomputing/
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.
