Explainable AI and Network Science for Social Systems and Collective Intelligence

CALL FOR PAPERS

Explainable AI and Network Science for Social Systems and Collective Intelligence

Journal: Information Processing & Management

Publisher: Elsevier

Submission Deadline: 31 March 2027

Introduction

As humans increasingly communicate in real time on digital platforms, networked online social systems are reshaping how information spreads, opinions interact, communities form, and collective decisions emerge. Yet research on these processes in advanced network models — such as multilayer networks coupled across multiple social platforms and higher-order networks that capture group interactions and complex communication patterns — remains limited.

Recent advances in artificial intelligence have provided powerful tools for modelling and analyzing behavior on these platforms. However, many AI-based models still operate as "black boxes" — making it difficult to explain or justify their outputs. This lack of transparency is critical in areas such as public opinion management, understanding the emergence of collective behaviors, and analyzing social influence. With generative AI, recommender engines, and autonomous agents now being deployed at scale, it is urgent to understand how AI technologies interact with network structure and dynamics — and how their interactions influence collective intelligence and decision-making.

Scope & Significance

This Special Issue aims to bring together explainable AI and network science to advance the study of social networks, information cascades, and the emergence of collective intelligence (CI). It invites research that integrates AI, machine learning, and data-driven methods with rigorous network modelling for networked social systems — including multilayer and higher-order networks.

The Special Issue offers a dedicated venue for interdisciplinary research on information flow, user behavior, and collective intelligence in social systems — combining modern AI methods with strong foundations in network science to advance both fundamental understanding and the development of trustworthy and practical solutions for real-world complex social systems.

List of Topic Areas

Manuscripts are invited on themes including, but not limited to:

1. Influence and leadership identification in dynamic networks — identifying task-specific influential users over time through explainable methods

2. Higher-order interactions and collective behavior beyond pairwise edges — hypergraphs, simplicial complexes, contagion, cooperation, and consensus

3. Reputation assessment and trust formation in human-AI systems — under noisy, biased, or adversarial settings

4. AI-driven collective decision-making under information disorder — misinformation, manipulation, and LLM-generated synthetic content

5. Human-AI collective intelligence in online communities — how LLMs as autonomous agents enhance or reduce collective performance

6. Knowledge graphs for explainable collective intelligence — integrating agents, content, contexts, and causal pathways

7. Graph learning for collective behavior prediction — temporal and higher-order GNNs for diffusion and coordination dynamics

8. LLM-based social simulation for behavior prediction — studying how LLM-driven agents interact in social systems

9. Early-warning signals and risk forecasting in complex social networks using AI and network science

10. Fairness, inequality, and polarization in AI-mediated social systems — how AI agents redistribute exposure, attention, and power

11. Collective intelligence mechanisms for complex systems — incentives, rules, and governance under uncertainty

12. Causal inference for platform interventions and policy evaluation — feedback loops and partial observability

13. Opinion dynamics, game theory, and multi-agent systems in social network analysis

14. Information propagation, influence maximization, and key user identification

15. Recommender systems and information source localization using XAI and GNNs

Guest Editors

Dr. Tao Wen Alliance Manchester Business School, The University of Manchester, UK Email: tao.wen@manchester.ac.uk

Dr. Xinyi Zhou Department of Computer Science, Boise State University, USA Email: xinyizhou@boisestate.edu

Prof. Richard Allmendinger Alliance Manchester Business School, The University of Manchester, UK Email: richard.allmendinger@manchester.ac.uk

Assoc. Prof. Kang Hao Cheong School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore Email: kanghao.cheong@ntu.edu.sg

Key Deadlines

Manuscript Submission Deadline: 31 March 2027

Submission Guidelines

Submit your manuscript to the Special Issue category via the online submission system of Information Processing & Management. When submitting, select Special Issue category:

"VSI: AINet"

All submissions should follow the general author guidelines of Information Processing & Management.

All submissions must be original and must not be under review elsewhere at the time of submission.

For author guidelines, visit the official Information Processing & Management journal page on the Elsevier ScienceDirect website.

About the Journal

Information Processing & Management, published by Elsevier, is a premier international peer-reviewed journal with a CiteScore of 18.6 and Impact Factor of 6.9. It supports open access publishing and is dedicated to advancing research on information retrieval, knowledge management, natural language processing, and the intersection of AI and information systems — providing a leading global platform for interdisciplinary scholarship exploring how information is processed, managed, and used across diverse digital and social contexts worldwide.

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