Advances in Graph-Structured Data: Methods and Applications

Dear Colleagues,

Graph-structured data offers a powerful and flexible abstraction for representing complex entities and their relationships. In many real-world scenarios, data can be naturally modeled as graphs. For example, users and their connections in social networks, concepts, and their relationships in knowledge graphs, or users, items, and their interactions in recommender systems.

This Special Issue focuses on graph-structured data in its broadest sense, welcoming recent advances across three interconnected areas: (1) graph-structured data management and storage, such as graph databases, RDF/triplestores, and property graph systems; (2) graph-structured data modeling and learning, such as graph representation learning, graph neural networks, and knowledge graph learning; (3) graph-structured data-driven real-world applications. These applications include, but are not limited to, the following:

• Recommender systems;
• Healthcare;
• Finance;
• Social computing;
• Transportation and smart cities;
• Cybersecurity;
• Scientific discovery;
• Education;
• Blockchain and Web3 systems

Dr. Liang Qu
Dr. Jingxian Cheng
Dr. Shangfei Zheng
Prof. Dr. Jianxin Li
Guest Editors

Manuscript Submission Information

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