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Mathematics
Special Issues
Recent Advances in Graph and Hypergraph: Theories, Models and Applications
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Recent Advances in Graph and Hypergraph: Theories, Models 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: 30 September 2026 | Viewed by 563

Special Issue Editor

Prof. Dr. Yong Zhang

E-Mail Website
Guest Editor
Beijing Institute of Artificial Intelligence, Department of Information Science, Beijing University of Technology, Beijing 100124, China
Interests: big data analysis; artificial intelligence; intelligent transportation; graphics and images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Graph and hypergraph theory has become an essential component of artificial intelligence and deep learning, extending classical graph concepts to represent higher-order and complex relationships. Recent advances in spectral methods, algebraic and topological approaches, and dynamic graph analysis have deepened theoretical understanding, while the rise of graph neural networks, hypergraph neural networks, and adaptive structure learning has provided powerful data-driven modelling tools. These advances have enabled broad applications in computer vision, natural language processing, spatiotemporal data mining, and recommendation systems. The purpose of this Special Issue is to collect original contributions on both the theoretical foundations and practical applications of graph and hypergraph theory, with a broad scope that encourages diverse perspectives and interdisciplinary research.

Prof. Dr. Yong Zhang
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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

  • graph theory
  • hypergraph theory
  • graph construction
  • hypergraph construction
  • graph neural networks
  • hypergraph neural networks
  • graph/hypergraph in computer vision
  • graph/hypergraph-based natural language processing
  • graph/hypergraph in spatiotemporal modeling
  • graph/hypergarph-based recommendation systems
  • LLM with graph/hypergraph

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Published Papers (1 paper)

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Research

17 pages, 448 KB  
Article
Leveraging Max-Pooling Aggregation and Enhanced Entity Embeddings for Few-Shot Knowledge Graph Completion
by Meng Zhang and Wonjun Chung
Mathematics 2025, 13(21), 3486; https://doi.org/10.3390/math13213486 - 1 Nov 2025
Viewed by 350
Abstract
Few-shot knowledge graph (KG) completion is challenged by the dynamic and long-tail nature of real-world KGs, where only a handful of relation-specific triples are available for each new relation. Existing methods often over-rely on neighbor information and use sequential LSTM aggregators that impose [...] Read more.
Few-shot knowledge graph (KG) completion is challenged by the dynamic and long-tail nature of real-world KGs, where only a handful of relation-specific triples are available for each new relation. Existing methods often over-rely on neighbor information and use sequential LSTM aggregators that impose an inappropriate order bias on inherently unordered triples. To address these limitations, we propose a lightweight yet principled framework that (1) enhances entity representations by explicitly integrating intrinsic (self) features with attention-aggregated neighbor context, and (2) introduces a permutation-invariant max-pooling aggregator to replace the LSTM-based reference set encoder. This design faithfully respects the set-based nature of triples while preserving critical entity semantics. Extensive experiments on the standard few-shot KG completion benchmarks NELL-One and Wiki-One demonstrate that our method consistently outperforms strong baselines, including non-LSTM models such as MetaR, and delivers robust gains across multiple evaluation metrics. These results show that carefully tailored, task-aligned refinements can achieve significant improvements without increasing model complexity. Full article
(This article belongs to the Special Issue Recent Advances in Graph and Hypergraph: Theories, Models and Applications)
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