Scalable Algorithms for Large-Scale Graph Neural Networks
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: 31 May 2026 | Viewed by 52
Special Issue Editor
Special Issue Information
Dear Colleagues,
Graph Neural Networks (GNNs) have emerged as powerful tools for learning from graph-structured data, with applications spanning social network analysis, recommendation systems, bioinformatics, and knowledge graphs. However, as real-world graphs grow to unprecedented scales—with millions of nodes and billions of edges—traditional GNN architectures face significant challenges in scalability, efficiency, and adaptability. This Special Issue focuses on innovative algorithmic solutions designed to overcome these barriers, enabling GNNs to operate effectively in large-scale and dynamic environments.
We invite contributions that explore novel methods for scaling GNNs, including but not limited to the following topics: sampling techniques, distributed and parallel training strategies, model compression, efficient attention mechanisms, hardware-aware optimization, and scalable inference frameworks. Submissions may also address theoretical foundations, empirical evaluations, and real-world case studies that demonstrate practical impact.
By bringing together cutting-edge research on scalable GNN algorithms, this Special Issue aims to advance the state of the art and foster next-generation graph learning systems that can handle the complexity and scale of modern graph data.
Prof. Dr. Ronghua Li
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. Algorithms is an international peer-reviewed open access monthly 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 1800 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 neural networks
- scalable algorithms
- distributed learning
- graph sampling
- deep learning on graphs
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
