Symmetry and Asymmetry in Machine Learning and Data Mining

Dear Colleagues,

Symmetry and asymmetry are fundamental principles in machine learning and data mining that govern how models capture and utilize structural properties of data. Symmetry refers to invariance or equivariance under transformations such as permutation, translation, or rotation, allowing learning algorithms to generalize by exploiting consistent and shared patterns. In contrast, asymmetry represents directional, imbalanced, or non-reciprocal relationships that frequently arise in real-world data, including causal dependencies, hierarchical structures, skewed class distributions, and asymmetric similarity relationships.

Rather than being merely theoretical concepts, symmetry and asymmetry have become central design considerations in modern learning systems. Recent studies show that explicitly incorporating these properties can enhance representation learning, robustness, and model interpretability. Symmetry-aware approaches, such as invariant and equivariant neural architectures, have demonstrated strong performance across structured data domains, while asymmetry-aware methods—including asymmetric loss functions, directed graph learning, imbalance-aware modeling, and asymmetric distance measures—offer effective solutions for handling complex, heterogeneous, and realistic data distributions.

This Special Issue aims to bring together theoretical insights and practical advances that leverage symmetry and asymmetry in machine learning and data mining, with a particular emphasis on data analytics, natural language processing, text mining, data science, big data analytics, and related fields. Topics of interest include symmetry- and asymmetry-aware representation learning, graph and relational data mining, domain adaptation, multimodal learning, and large-scale data analysis. Through the integration of methodological innovations and real-world applications, this Special Issue seeks to advance a deeper understanding of symmetry- and asymmetry-driven learning in intelligent data-driven systems.

Dr. Jantima Polpinij
Dr. Olarik Surinta
Dr. Manasawee Kaenampornpan
Guest Editors

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. Symmetry 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 2400 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.