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Mathematics
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Advanced Research in Complex Networks and Social Dynamics
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Advanced Research in Complex Networks and Social Dynamics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1344

Editor

Dr. Keke Shang

E-Mail Website
Guest Editor
School of Journalism and Communication, Nanjing University, Nanjing 210000, China
Interests: complex networks; social physics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Presently, the convergence of complex network theory, social physics, and data-driven artificial intelligence is reshaping our understanding of human interaction, information flow, and collective behavior in digital societies. This Special Issue aims to showcase cutting-edge research that advances the modeling, analysis, and prediction of social dynamics through the framework of complex networks. We welcome interdisciplinary contributions that integrate statistical physics, machine learning, computational social science, and empirical network analysis to uncover fundamental mechanisms governing online and offline social systems, including link formation, community evolution, misinformation cascades, opinion polarization, and coupled epidemic–information spreading.

Dr. Keke Shang
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

  • complex networks
  • social dynamics
  • social physics
  • computational social science
  • link prediction
  • information diffusion
  • opinion polarization
  • multilayer networks
  • temporal networks
  • graph neural networks
  • physics informed machine learning
  • community detection
  • null models
  • computational communication
  • ai for social systems

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Published Papers (2 papers)

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23 pages, 4200 KB  
Article
A Network-Cascade Framework for Short-Run Production Failure Under Maritime-Energy Chokepoint Disruption
by Feng An, Shuai Ren, Xuyang Liu, Siyao Liu and Jingwen Cui
Mathematics 2026, 14(10), 1708; https://doi.org/10.3390/math14101708 - 15 May 2026
Viewed by 293
Abstract
Abrupt maritime-energy disruption can generate system-wide production losses before firms and policymakers can adjust. Existing assessments usually emphasize direct exposure or long-run equilibrium responses, which makes them less suitable for short-run risk assessment in energy-dependent production systems. We develop a threshold-cascade framework that [...] Read more.
Abrupt maritime-energy disruption can generate system-wide production losses before firms and policymakers can adjust. Existing assessments usually emphasize direct exposure or long-run equilibrium responses, which makes them less suitable for short-run risk assessment in energy-dependent production systems. We develop a threshold-cascade framework that combines dual-track dependence topology, edge-level inventories, smooth operability bands, and a separate price-validation step to identify the blockade intensity at which a localized chokepoint shock becomes systemic production loss. The framework is evaluated against the March 2021 Suez blockage and the 2022 Russia–Ukraine producer-price episode, and then applied to a 2026 Strait of Hormuz stress scenario using the Organisation for Economic Co-operation and Development (OECD) Inter-Country Input-Output (ICIO) tables, 2025 edition, with the 2022 benchmark year. Under the baseline 150-day horizon, terminal loss first reaches 50% at about 32% blockade intensity, with a broader calibrated threshold band of 32–46%. Losses spread beyond the point of origin and become concentrated in East and Southeast Asian manufacturing supply chains and in downstream consumer markets after inventories at connected hubs are depleted. Policy experiments show that single-channel interventions shift the threshold only modestly, whereas an integrated package that relaxes logistics, inventories, and upstream scarcity moves the threshold to about 46% in this calibration. The analysis targets the weeks-to-months interval before substitution, contract renegotiation, and broader market adjustments dominate. Within that interval, the model identifies when buffers fail, how production losses spread, and which intervention packages delay systemic disruption. Full article
(This article belongs to the Special Issue Advanced Research in Complex Networks and Social Dynamics)
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21 pages, 2870 KB  
Article
Optimizing Social Media Campaigns Through Engagement Topology and Behavioral Clustering
by Tichaona Chikore, Moster Zhangazha and Farai Nyabadza
Mathematics 2026, 14(9), 1466; https://doi.org/10.3390/math14091466 - 27 Apr 2026
Viewed by 560
Abstract
Social media engagement drives both individual behavior and content dissemination, yet traditional analytics often reduce interactions to simple counts, obscuring the complex structures underlying user activity. In the highly competitive digital landscape, understanding how users interact with content is crucial for businesses aiming [...] Read more.
Social media engagement drives both individual behavior and content dissemination, yet traditional analytics often reduce interactions to simple counts, obscuring the complex structures underlying user activity. In the highly competitive digital landscape, understanding how users interact with content is crucial for businesses aiming to optimize social media campaigns and maximize return on investment (ROI). Traditional engagement metrics, such as likes and shares, fail to capture the underlying structure and dynamics of user behavior. This study investigates the latent patterns of engagement by combining topological data analysis (TDA) with behavioral clustering across 100,000 posts on multiple platforms. Using persistent homology and k-nearest neighbour graphs, we reveal a primary bifurcation between Active (validation-focused) and Passive (consumption/propagation) users, nested four-strain substructures, and over 650 significant H1 loops indicating recurring feedback cycles. Active users exhibit strong cluster cohesion and high engagement rates, while Passive users contribute broadly to content diffusion with slightly higher loop counts, highlighting distinct functional roles in social media dynamics. These findings provide a principled framework for targeting content, reinforcing feedback loops, and leveraging hub posts to amplify engagement. By linking topological structure to behavioral patterns, this work advances both the theoretical understanding of digital interaction and the practical design of more effective social media campaigns. Full article
(This article belongs to the Special Issue Advanced Research in Complex Networks and Social Dynamics)
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