Information-Theoretic Approaches for Network Data Mining: Causality, Complexity and Scalability
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 1 January 2026 | Viewed by 19
Special Issue Editors
Interests: big data; recommender systems
Special Issues, Collections and Topics in MDPI journals
Interests: big data; neural network; internet of things; collaborative filtering; matrix factorization; quality of service; computational linguistics; natural language processing systems; language modeling
Interests: natrual language processing; AIoT (Artificial Intelligence of Things); text mining; generative AI; recommender systems; web service
Special Issues, Collections and Topics in MDPI journals
Interests: cloud/edge computing; scalable machine learning; data privacy and cybersecurity
Special Issues, Collections and Topics in MDPI journals
Interests: service computing; social networks; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid growth of networked systems—spanning communication networks, social platforms, biological systems, and cyber–physical infrastructures—has led to an explosion of structured, high-dimensional, and dynamic data. Extracting meaningful, interpretable, and actionable knowledge from such network data remains a fundamental challenge in modern data science. Information theory offers a powerful and principled framework for understanding the structure and dynamics of complex networks. Concepts such as entropy, mutual information, information flow, and algorithmic complexity have emerged as critical tools for analyzing uncertainty, causality, and interactions within large-scale networks. When applied to network data mining, these tools not only enhance interpretability and robustness, but also address scalability and efficiency challenges inherent in real-world applications.
This Special Issue invites high-quality contributions that advance the theoretical foundations, algorithmic development, and practical applications of information-theoretic approaches in network data mining. We are particularly interested in studies that address emerging challenges related to causal inference, multiscale complexity, structural and temporal dynamics, and scalable inference algorithms. Works grounded in entropy-based modeling, information-theoretic learning, and probabilistic inference are especially encouraged, as are those exploring interdisciplinary intersections—such as neuroscience, social networks, biological systems, and distributed sensor networks.
Topics of interest include, but are not limited to, the following:
- Entropy-based methods for network structure learning and community detection;
- Information-theoretic causality in dynamic and temporal networks;
- Complexity measures in multilayer, heterogeneous, or evolving networks;
- Scalable information-theoretic algorithms for large-scale graph mining;
- Mutual information and its variants for feature selection and node/edge ranking in graphs;
- Information flow analysis in distributed and decentralized networks;
- Entropy and uncertainty quantification in graph-based deep learning models;
- Applications in neuroscience, epidemiology, cyber-physical systems, and social networks using information-theoretic frameworks;
- Theoretical foundations of entropy and information in network generative models;
- Data-driven approaches for network anomaly detection based on entropy divergence and information gain.
Prof. Dr. Lianyong Qi
Dr. Wenwen Gong
Dr. Yang Zhang
Dr. Xuyun Zhang
Dr. Yanwei Xu
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. Entropy 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 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
- networked systems
- information theory
- complex networks
- algorithmic complexity
- network data mining
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.
