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Mathematics
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Data Analysis for Social Networks and Information Systems
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Data Analysis for Social Networks and Information Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2631

Editor

Dr. Hamid Karimi

E-Mail Website
Guest Editor
Department of Computer Science, Utah State University, Logan, UT 84322, USA
Interests: machine learning; data mining; data science; social network analysis; social media mining; educational data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on "Data Analysis for Social Networks and Information Systems" invites contributions that explore the mathematical computing and algorithmic approaches to analyzing complex social networks and information systems. This Special Issue aims to advance our understanding of how data-driven methods can be applied to uncover patterns, dynamics, and insights from social network structures and information flows. We welcome submissions that focus on the development and application of advanced algorithms, such as graph-based models, machine learning techniques, optimization algorithms, and statistical methods, to tackle challenges in network analysis. Potential topics include, but are not limited to, community detection, network evolution, influence propagation, and the integration of heterogeneous data sources. The goal is to foster interdisciplinary research that bridges the gap between theoretical foundations and practical applications in social network analysis and information system optimization. Authors are encouraged to present novel methodologies, case studies, or comprehensive reviews that contribute to this evolving field.

Dr. Hamid Karimi
Guest Editor

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-anonymized 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 analysis
  • information systems
  • mathematical computing
  • algorithm design
  • graph theory
  • machine learning
  • community detection
  • network evolution
  • network dynamics
  • influence propagation
  • big data analytics
  • statistical methods

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Published Papers (3 papers)

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Research

22 pages, 759 KB  
Article
Self-Triggered Switched ISS Framework Under Computational Weaponization
by Mordecai Opoku Ohemeng and Frederick T. Sheldon
Mathematics 2026, 14(16), 2871; https://doi.org/10.3390/math14162871 - 7 Aug 2026
Viewed by 205
Abstract
Networked Cyber–Physical Systems (CPSs), like autonomous quadrotor swarms, tightly couple continuous physical kinematics, wireless information exchange, and discrete real-time task scheduling. While conventional consensus security architectures focus exclusively on data-layer falsification, they fundamentally decouple adversarial behavior from onboard computational resource state profiles. This [...] Read more.
Networked Cyber–Physical Systems (CPSs), like autonomous quadrotor swarms, tightly couple continuous physical kinematics, wireless information exchange, and discrete real-time task scheduling. While conventional consensus security architectures focus exclusively on data-layer falsification, they fundamentally decouple adversarial behavior from onboard computational resource state profiles. This paper addresses a core CPS vulnerability termed Computational Weaponization, the deliberate injection of complex computational workloads (adversarial LLM token parsing or cryptographic verification) to intentionally manipulate hardware execution delays. Through this exploit, strategic cyber–physical perturbations force resource-constrained embedded microcontrollers to saturate their task execution queues, inducing real-time scheduling starvation and physical tracking divergence. To mitigate this without optimization bottlenecks, we present a state-dependent, Self-Triggered Control (STC) and Prospect Theoretic Alignment (PTA) co-design framework. The proposed protocol models the hardware microprocessor’s execution delay as an endogenous dynamic state coupled directly to continuous tracking spaces. By mapping discrete topology reconfigurations and variable task delays to a switched impulsive time-delay system, we leverage an Input-to-State Stability (ISS) to derive sufficient linear matrix inequality conditions. We prove that the coupled cyber–physical–computational loop achieves asymptotic consensus and bounded trajectory containment under adversarial actions. Full article
(This article belongs to the Special Issue Data Analysis for Social Networks and Information Systems)
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30 pages, 2089 KB  
Article
RSCF-PM: Relation-Specific Curvature Fields on Product Manifolds for Fraud Detection in Multi-Relational Social Networks
by Yuchen Yang, Hongli Zhang and Gongzhu Yin
Mathematics 2026, 14(11), 1804; https://doi.org/10.3390/math14111804 - 23 May 2026
Viewed by 267
Abstract
Graph-based fraud detection in multi-relational social networks must capture heterogeneous relation semantics and diverse fraud patterns while preserving geometric consistency and remaining scalable. Existing methods often either force all relations into a shared Euclidean or single-curvature space, or fuse relation-wise embeddings after mapping [...] Read more.
Graph-based fraud detection in multi-relational social networks must capture heterogeneous relation semantics and diverse fraud patterns while preserving geometric consistency and remaining scalable. Existing methods often either force all relations into a shared Euclidean or single-curvature space, or fuse relation-wise embeddings after mapping them to tangent coordinates, which weakens curvature-dependent metric information. We propose Relation-Specific Curvature Fields on Product Manifolds (RSCF-PM), a geometry-consistent framework that learns relation-specific curvature and represents each node as a tuple on a Riemannian product manifold. Each relation is encoded in its own hyperbolic space, and cross-relation fusion is performed directly through the product metric rather than Euclidean concatenation. On top of this representation, we introduce a multi-prototype classifier to model multiple fraud modes within each class. To support large-scale training, we adopt tangent-space aggregation as an efficient approximation to the Fréchet mean. Experiments on four public fraud detection benchmarks, including the 5.78M-node T-Social network, show that RSCF-PM achieves the best results on T-Social, FDCompCN, and YelpChi, while remaining highly competitive on Amazon, with up to 4.96% AUC improvement over strong baselines. Ablation and efficiency studies further confirm the complementary value of each component and the practical scalability of the framework. Full article
(This article belongs to the Special Issue Data Analysis for Social Networks and Information Systems)
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24 pages, 5820 KB  
Article
NSLS: A Neighbor Similarity and Label Selection-Based Algorithm for Community Detection
by Shihu Liu, Hui Chen, Shuang Li and Xiyang Yang
Mathematics 2025, 13(8), 1300; https://doi.org/10.3390/math13081300 - 16 Apr 2025
Viewed by 1193
Abstract
Community detection is still regarded as one of the most applicable methods for discovering latent information in complex networks. Recently, many similarity-based community detection algorithms have been widely applied to the analysis of complex networks. However, these approaches may also have some limitations, [...] Read more.
Community detection is still regarded as one of the most applicable methods for discovering latent information in complex networks. Recently, many similarity-based community detection algorithms have been widely applied to the analysis of complex networks. However, these approaches may also have some limitations, such as relying solely on simple similarity measures, which makes it difficult to differentiate the tightness of the relation between nodes. Aiming at this issue, this paper proposes a community detection algorithm based on neighbor similarity and label selection (NSLS). Initially, the algorithm assigns labels to each node using a new local similarity measure, thereby quickly forming a preliminary community structure. Subsequently, a similarity parameter is introduced to calculate the similarity between nodes and communities, and the nodes are reassigned to more appropriate communities. Finally, dense communities are obtained by a fast-merge method. Experiments on real-world networks show that the proposed method is accurate, compared with recent and classical community detection algorithms. Full article
(This article belongs to the Special Issue Data Analysis for Social Networks and Information Systems)
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