Machine Learning, Data Mining, and Optimization Methods for Graphs

Dear Colleagues,

This Special Issue focuses on advancing mathematical computing and algorithmic methodologies for graphs in the context of machine learning, data mining, and optimization. Graphs are versatile structures for modeling complex relationships in various fields, including social networks, biological systems, and industrial and communication networks. Recent developments in graph algorithms, graph neural networks, and combinatorial optimization have opened new opportunities for solving challenging problems involving graph-structured data.

We invite original contributions that explore novel mathematical models, computational techniques, and algorithmic innovations aimed at improving learning, inference, and optimization in graphs. Topics of interest include, but are not limited to, graph-based machine learning algorithms, spectral graph theory, scalable graph mining techniques, combinatorial optimization on large-scale graphs, and applications in network analysis. This Special Issue aims to bridge the gap between theoretical advances in graph algorithms and their practical applications, providing a platform for researchers to present state-of-the-art methodologies and real-world implementations.

Potential authors are encouraged to submit high-quality manuscripts that contribute to the mathematical foundations, computational strategies, and algorithmic frameworks for graph-based problems.

Dr. Yang Lou
Guest Editor

Dr. Xiaowen Bi
Guest Editor Assistant

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-anonymized 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.