Advances in Applied Data Mining and Social Network Analysis

Dear Colleagues,

This Special Issue, titled “Advances in Applied Data Mining and Social Network Analysis,” seeks to present cutting-edge research that integrates mathematical rigor with impactful applications in data mining and social network analysis. In the era of ubiquitous digital interactions, vast volumes of structured and unstructured data are continuously generated from platforms such as social media, financial markets, and cyber-physical systems. These data streams pose both analytical challenges and opportunities for novel mathematical modeling. We invite submissions that contribute to the theoretical foundations and methodological advancements in this field, including, but not limited to,

• Mathematical models of network dynamics, such as random graph theory, stochastic block models, and spectral graph theory;
• Theoretical developments in community detection, influence maximization, and diffusion processes, incorporating tools like graph Laplacians, matrix factorization, and eigenvalue perturbation;
• Optimization-based frameworks, including convex and non-convex formulations for large-scale data mining problems, along with proofs of convergence and complexity bounds;
• Applications of deep learning models, such as graph neural networks (GNNs), transformers, and large language models (LLMs), particularly their mathematical architectures, representational capacities, and training dynamics;
• Multimodal data integration, leveraging information-theoretic measures, kernel methods, or tensor decomposition techniques;
• Unresolved mathematical challenges, such as the scalability of inference in high-dimensional networks, robustness to adversarial inputs, and the interpretability of learned models.

This Special Issue particularly encourages papers that bridge methodological innovation with real-world applications in FinTech, behavioral analytics, misinformation detection, recommendation systems, and mining systems and engineering. Contributions should demonstrate strong theoretical grounding, offer new analytical insights, and preferably include real-world datasets, formal analysis, and reproducible experiments. Through this Special Issue, we aim to foster dialog between mathematical theory and data-intensive applications, advancing our understanding of complex socio-technical systems.

Prof. Dr. Yongli Li
Prof. Dr. Tingxin Wen
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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• applied data mining
• social network analysis
• complex networks
• large language models
• multimodal learning
• FinTech
• mining systems and engineering