Artificial Intelligence in Network Science
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 494
Special Issue Editors
Interests: artificial intelligence; network science; machine learning; graph theory and probabilistic method
Special Issue Information
Dear Colleagues,
Recent interests in network data with multilayer and heterogeneous structures pose a significant challenge to traditional graph-theoretic and statistical approaches that have played important roles in network science. These multiplex and heterogeneous networks naturally arise in many research fields and industrial practices such as computational biology, social media, and knowledge graphs. To address the challenge, techniques and models in Artificial Intelligence have the potential (and are increasingly being used) to complement traditional approaches to network analysis or to help solve algorithmic and computational problems that are beyond the reach of those more standard approaches.
This Special Issue is dedicated to showcasing recent progress in applying AI techniques to the analysis and modeling of complex networks. All papers are welcome that describe work on using AI techniques in a substantial way to solve algorithmic problems in network analysis (such as those related to centrality measures, meso-scale structures, network embedding, role analysis, and network diffusion), to model network phenomena and evolution, or to understand the dynamic processes taking place on networks. Of particular interest are papers that report original work on the use of AI problem-solving methods, deep representation learning techniques, and game-theoretic models to address problems in the analysis of multiplex networks and in the integration of networked data.
Dr. Yong Gao
Dr. James Nastos
Guest Editors
Manuscript Submission Information
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Keywords
- network science
- multiplex networks
- meso-scale structures
- network embedding
- dynamics and evolution of complex networks
- problem-solving methods
- reasoning and search
- graph representation learning
- deep learning
- integration of networked data
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