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Information Processing & Management (IPM)

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Impact Factor:
6.9
Publisher:
Elsevier
ISSN:
0306-4573
Viewed:
62009
Tracked:
91

Call For Papers

Information Processing & Management (IPM) is an academic journal published by Elsevier. (ISSN 0306-4573, impact factor 6.9, CCF B).

This journal is ranked by The Chartered Association of Business Schools' Academic Journal Guide, Australian Business Deans Council, Chinese Academy of Sciences (CAS), China Computer Federation (CCF), BFI (Denmark), Computing Research & Education (CORE) Journal Ranking, The Publication Forum (Finland), Science Citation Index Expanded, Social Sciences Citation Index, Scopus, and SCImago Journal Rank (SJR). Information Processing and Management publishes cutting-edge original research at the intersection of computing and information science concerning theory, methods, or applications in a range of domains, including but not limited to advertising, business, health, information science, information technology marketing, and social computing. The journal aims to serve the interests of primary researchers but also practitioners in furthering knowledge at the intersection of computing and information science by providing an effective forum for the timely dissemination of advanced and topical issues. The journal is especially interested in original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Specifically, the journal is interested in four types of manuscripts, which are: Research manuscripts addressing topics at the intersection of computer and information science. Methods manuscripts focusing on the application of novel methods at the intersection of computer and information science. Review manuscripts assessing, in a critical and in-depth manner, a broad trend at the intersection of computer and information science, providing integration of the prior research, and recommendations for further work in the area. Critical application manuscripts concerning system design research at the intersection of computer and information science.
Last updated by Dou Sun on

Special Issues

Special Issue on Explainable AI and Network Science for Social Systems and Collective Intelligence Submission Date: 2027-03-31 Guest editors: Tao Wen, Research Fellow, Alliance Manchester Business School, The University of Manchester, Manchester, UK Email: [email protected] Xinyi Zhou, Assistant Professor, Department of Computer Science, Boise State University, Idaho, USA Email: [email protected] Richard Allmendinger, Professor, Alliance Manchester Business School, The University of Manchester, Manchester, UK Email: [email protected] Kang Hao Cheong, Associate Professor, School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore, Singapore Email: [email protected] Special issue information: 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. At the same time, recent advances in artificial intelligence (AI) 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 (CI) and decision-making. 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 CI. We invite research that integrates AI, machine learning, and data-driven methods with rigorous network modelling for networked social systems, including multilayer and higher-order networks. Topics of interest include the use of explainable AI (XAI), graph neural networks (GNNs), large language models (LLMs), causal inference, and optimization algorithms to study information propagation, influence maximization, key user identification, recommender systems, and information source localization. We also welcome studies that apply responsible AI principles to explain and model human collective decision-making and CI, including consensus formation, polarization, cooperation, coordination, and innovation diffusion. Methodologies may include opinion dynamics, game theory, graph learning, reinforcement learning, multi-agent systems, and large-scale data mining. This special issue offers a dedicated venue for interdisciplinary research on information flow, user behavior, and CI in social systems. By combining modern AI methods with strong foundations in network science, it seeks to advance both fundamental understanding and the development of trustworthy and practical solutions for real-world complex social systems. Possible Topics of Submissions: This special issue is interested in, but not limited to, the following topics: Influence and leadership identification in dynamic networks: Identify task-specific influential users over time in dynamic and multiplex networks through explainable methods. Higher-order interactions and collective behavior beyond pairwise edges: How hypergraphs and simplicial complexes affect contagion, cooperation, consensus, and collective decision-making. Reputation assessment and trust formation in human-AI systems: How to assess AI agent reputation and build trust for informed human-AI decisions in noisy, biased, or adversarial settings. AI-driven collective decision-making under information disorder: How groups can make informed decisions under misinformation, manipulation, and LLM-generated or synthetic content. Human-AI collective intelligence in online communities: How collective performance is enhanced or reduced when LLMs participate as autonomous agents on platforms and communities. Knowledge graphs for explainable collective intelligence: Use knowledge graphs to represent and explain collective intelligence by integrating agents, content, contexts, and causal pathways. Graph learning for collective behavior prediction: Forecast diffusion, coordination, and group decision dynamics under noise and uncertainty by graph learning (e.g. temporal and higher-order GNNs). LLM-based social simulation for behavior prediction: Study how LLM-driven agents interact in social systems to explain and predict diffusion, coordination, and collective behaviors. Early-warning signals and risk forecasting in complex social systems: Detect early-warning signals and predict cascading risks in complex social networks using AI and network science. Fairness, inequality, and polarization in AI-mediated social systems: How AI agents redistribute exposure, attention, and power, potentially amplifying inequality and polarization. Collective intelligence mechanism for complex systems: Explore incentives, rules, and governance mechanisms that enable reliable collective intelligence under uncertainty and heterogeneity. Causal inference for platform interventions and policy evaluation: Estimate the causal effects of platform and policy interventions in the presence of feedback loops and partial observability. Manuscript submission information: Submit your manuscript to the Special Issue category (VSI: AINet) through the online submission system of Information Processing & Management. All the submissions should follow the general author guidelines of Information Processing & Management. Keywords: Explainable AI, LLM social simulation, Network science, Complex systems, Decision-making, Collective intelligence https://www.sciencedirect.com/special-issue/332020/explainable-ai-and-network-science-for-social-systems-and-collective-intelligence
Last updated by Dou Sun on

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