Opportunities and Challenges of Network Science in the Age of AI
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 27
Special Issue Editors
Interests: complex networks; network information mining; higher-order network analysis; ranking nodes; link prediction
Special Issue Information
Dear Colleagues,
Rapid advancements in Artificial Intelligence (AI), particularly the emergence of sophisticated generative models, are profoundly impacting various scientific disciplines. Network science, with its powerful framework for modeling and analyzing complex systems of interconnected entities, stands at a crucial intersection with AI. This Special Issue aims to explore the synergistic opportunities and inherent challenges arising from this convergence. We invite researchers to contribute cutting-edge work that investigates how network science can empower AI methodologies and, conversely, how AI, including generative AI, can revolutionize the study of networks.
This Special Issue will delve into two primary themes:
- Network Science for AI: harnessing the principles and tools of network science to enhance the capabilities and understanding of AI systems. Potential topics include the following:
- Network-inspired Architectures for AI;
- Graph Representation Learning;
- Network Analysis for Explainable AI (XAI);
- Network-based Feature Engineering for AI;
- Dynamics of Networks for AI;
- Network Science for Robust and Fair AI;
- Entropy and Information Theory in AI Systems;
- Statistical Physics of AI Learning.
- AI for Network Science: leveraging AI techniques, including generative AI, to advance the frontiers of network analysis, modeling, and discovery. Potential topics include the following:
- AI-driven Network Discovery and Reconstruction;
- AI-driven Network Generation;
- AI for Dynamic Network Analysis;
- Intelligent Network Visualization and Interpretation;
- AI-assisted Network Modeling and Simulation;
- Generative AI for Novel Network Design and Optimization;
- AI for Anomaly Detection and Security in Networks;
- Statistical Physics-informed Network Generation;
- Entropy Maximization and Information Flow in AI-generated Networks.
We invite contributions of original research articles, reviews, and perspectives on this cutting-edge topic from researchers in computer science, physics, biology, engineering, and social sciences.
Prof. Dr. Linyuan Lü
Prof. Dr. Qingchun Meng
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- network science
- complex networks
- artificial intelligence (AI)
- graph machine learning
- generative AI
- entropy
- statistical physics
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