Advances in Graph Computing: Algorithms and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 864
Editors
Interests: graph representation learning; LLM; multimodal learning; self-supervised learning; theory-driven efficient learning algorithms
Interests: big data management and analysis; social network analysis; open-source intelligence analysis
Interests: big data; artificial intelligence; data security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Graph computing plays an increasingly important role in the development of modern data science and scientific computing. It aims to study the mathematical foundations, algorithmic principles, and practical implementations of computation over graph-structured data, enabling effective analysis, learning, optimization, and inference in complex systems. Applications and examples of graph-based models and methods are ubiquitous across a diverse set of areas, including network science, knowledge graphs, recommendation, communication networks, biological and chemical networks, transportation systems, and social and information networks. The growing demand for scalable and reliable methods for understanding relational and structured data, as well as their deployment in real-world systems, is driving rapid advances in graph computing.
In this Special Issue, we aim to present recent developments in the theory and applications of graph computing, with a special emphasis on graph algorithms and combinatorial optimization, spectral and algebraic methods for graph analysis, self-supervised learning on graphs, scalable and distributed graph processing, graph sampling, dynamic and heterogeneous graphs, and graph representation learning (including graph neural networks), as well as robustness, interpretability, and trustworthy graph analytics.
Dr. Rong Yin
Dr. Can Ma
Prof. Dr. Weiping Wang
Guest Editors
Manuscript Submission Information
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Keywords
- graph algorithms and combinatorial optimization
- spectral, algebraic, and topological methods for graph analysis
- scalable and distributed graph processing
- graph representation learning and graph neural networks
- applications of graph computing in the natural sciences (e.g., biology, chemistry)
- applications of graph computing in industry (e.g., advertising, recommender systems)
- applications of graph computing in the social sciences (e.g., social networks, information diffusion)
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