Social, communication, and data networks play an important role in information dissemination. Modern social media sites have hundreds of millions of users, and information spreads there in cascading fashion. Understanding how the information propagates may help to boost the business for companies and organizations as well as help the governments to avoid cybersecurity risks. Besides that, understanding of cascading processes in social media may contribute to understanding of propagation of failures in power networks, and propagation of shocks over economic networks.
CSoNet 2021 will feature a special track on analysis of information spread in social and data networks. The goal of this track is to provide a venue to disseminate research that is focused on analysis of information propagated over social media, and other types of networks, focusing on algorithmic methods that rely on network optimization and graph theory, robust and stochastic optimization, big data management and data analytics.
The scope of the special track includes (but is not limited to) the following topics:
- Analysis of information cascades at social media sites
- Propagation of shocks in economic networks
- Natural Language Processing for Social Media
- Optimization methods for social media and networks
- Epidemiology and networks
- Machine learning for networks
- Analysis of telecommunication networks
- Security and privacy in online social networks
- Anomaly detection algorithms and applications
Accepted papers will be published in the conference proceedings; also, extended versions of selected best papers will be invited for publication in Journal of Combinatorial Optimization, IEEE Transactions on Network Science and Engineering, and Computational Social Networks.
Track chair: Dr. Alexander Semenov (incoming, University of Florida) email@example.com
We follow the manuscript submission, review methods and deadlines, set by CSoNet 2021.