Deep Representation Learning for Social Network Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 51
Special Issue Editors
Interests: social networks and social applications; computational-data-mining reinforcement learning; multi-agent systems and autonomous agents; opinion dynamics
Special Issue Information
Dear Colleagues,
I invite you to submit your latest research in the area of mathematical optimization to this Special Issue titled “Deep Representation Learning for Social Network Analysis” in the journal Mathematics. Social network analysis arises in all fields in the real world and has immense importance. Deep representation learning, as an emerging method, provides new ideas and solutions for social network analysis. High-quality papers that address both theoretical and practical issues in the area of deep representation learning for social network analysis and submissions that present new theoretical results, models, and algorithms, as well as new applications, are welcome. Potential topics include, but are not limited to, social network structure analysis, the influence of users’ social network nodes, community discovery, dynamic social network analysis, user behavior, link prediction, social recommendation systems, information dissemination, anomaly detection, data mining, and other research problems.
Dr. Qiang He
Dr. Jianxiong Guo
Guest Editors
Manuscript Submission Information
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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.
Keywords
- social network structure analysis
- the influence of users’ social network nodes
- community discovery
- dynamic social network analysis
- user behavior
- link prediction
- social recommendation systems
- information dissemination in social networks
- anomaly detection
- social network data mining
- predicting future development trends in social network data
- node-embedded learning
- influence maximization
- infectious disease analysis
- malicious rumor control
- rumor detection
- healthcare applications
- machine learning
