Call for Papers

Workshop on Information Spread in Social Networks and AI-Based Agent Networks

CSoNet 2026

CSoNet 2026 will feature a workshop on the analysis of information spread in social and AI-based agent networks. The goal of this workshop is to provide a venue for disseminating research focused on the analysis of information propagation over social media and other types of networks, with emphasis on algorithmic methods based on network optimization and graph theory, robust and stochastic optimization, machine learning, natural language processing, big data management, and data analytics.

Social, communication, economic, and AI-based agent networks play an important role in information dissemination and decision-making processes. Modern social media platforms have hundreds of millions of users, and information often spreads through these platforms in a cascading fashion. Understanding how information propagates can help companies, organizations, and public agencies improve decision-making, outreach, and response strategies. Beyond social media, cascading processes are also important in many other networked systems, including the propagation of failures in power networks, the spread of shocks in economic networks, and the diffusion of knowledge and practices through communities. For example, in domains such as agriculture, network-based models can help analyze how information about new technologies, sustainable practices, and market conditions spreads among farmers, producers, extension services, and local communities. Recent advances in large language models, LLM-based agents, and AI-based agent networks further create new opportunities and challenges for modeling, analyzing, and optimizing information spread in complex networked environments.

Topics of Interest

The scope of the workshop includes, but is not limited to, the following topics:

• Analysis of information cascades in social media
• Propagation of shocks in economic networks
• LLM agents and AI-based agent networks
• Multi-agent systems and agent-based modeling of information spread
• Network epidemiology and spreading processes
• Analysis of telecommunication and communication networks
• Optimization methods for information propagation and networked systems, including robust and stochastic optimization
• Machine learning for networks
• Natural language processing for social media and online platforms
• Anomaly detection algorithms and applications
• Security and privacy in online social networks
• Network-based modeling of information diffusion in agriculture and rural communities
• Network-based applications in healthcare, transportation, infrastructure, and economics

All deadlines follow the manuscript submission procedures, review process, and deadlines established by CSoNet 2026.

Submission Instructions

Papers should be submitted through the CSoNet 2026 submission system, available at https://meteor.springer.com/CSoNet2026. When submitting, please select the "Workshop on Information Spread in Social Networks and AI-Based Agent Networks" as the track for your paper. Submissions must follow the CSoNet 2026 formatting guidelines and page limits; see the conference website for details.

Publication

Accepted papers will be published in the conference proceedings. Extended versions of selected best papers will be invited for publication in the Journal of Combinatorial Optimization and IEEE Transactions on Network Science and Engineering.

Workshop Chair

Dr. Alexander Semenov, University of South Florida, asemenov@usf.edu