Guest Editors
Dr. John Violos, National Technical University of Athens
Dr. Fotis Aisopos, National Centre of Scientific Research Demokritos
Summary
Recent advancements in Artificial Intelligence (AI) and networking reveal that the knowledge is not merely hidden in the content of the communication among entities but also in the context of the entities, how they are connected and how they interact. The graph structure can efficiently represent the interaction, the connectivity and the associativity among entities. Furthermore, using neural networks we can leverage the graph representations in order to make optimal decision making. In this way Graph Neural Networks (GNNs) combine the assets of graphs with neural networks and bring to the fore methods and techniques such as graph embeddings and knowledge graphs refinement that researchers should investigate. GNNs constitute a model on which the knowledge can be distributed in different areas of the network on which peer agents can act, learn, make communities and their synergy is transformed into collaborative intelligence.
This special issue aims to present the applicability of GNNs for a variety of decision making and knowledge extraction challenges on which the knowledge is distributed in different areas. We invite authors to contribute with original, highquality and innovative methods, techniques and their applications. Articles can cover scientific and technical perspectives of AI but also the ethical perspectives of AI. We are interested in the use of AI for enhancement of human skills but also explain and reason the AI. We welcome articles that discuss how AI can come closer to human beings and serve as a means to prosperity.
Keywords
• Graph Deep Reinforcement Learning
• Knowledge Graphs Refinement
• Graph Embeddings
• Graph Convolutional Networks and Applications
• Graph Neural Networks on Semantic Web
• Collaborative & Distributed Knowledge
• Population based and Swarm Intelligence in Distributed Environments
• Data Quality Enhancement using AI
• Explainable AI
• Pervasive AI
• Ethics of AI
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