Data Mining Algorithms and Mathematical Models for Social Network Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 1 March 2026 | Viewed by 53
Special Issue Editor
Special Issue Information
Dear Colleagues,
Social networks have become a cornerstone of modern data analysis, offering rich insights into human behavior, communication patterns, and social structures. This Special Issue focuses on the intersection of data mining algorithms and mathematical models tailored for social network analysis, aiming to advance our understanding of complex relational data. We invite contributions on new approaches or techniques for addressing challenges such as community detection, influence propagation, fake news detection, and dynamic network modeling. Topics of interest include, but are not limited to, machine learning approaches for social network analysis, mathematical modeling of network evolution, graph-based algorithms for social network data, and applications in domains such as fake news detection, recommendation systems, echo chambers, and the science of science. By bridging theoretical advancements with practical applications, this Special Issue will foster interdisciplinary collaboration and highlight innovative solutions that push the boundaries of social network analysis. We welcome research that not only addresses computational challenges but also explores novel methodologies to extract meaningful insights from ever-growing and interconnected datasets.
Dr. Chenbo Fu
Guest Editor
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Keywords
- data mining algorithms
- mathematical models
- social network analysis
- artificial intelligence
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