Network and Hypernetwork Science: Emerging Models, Paradigms, and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 8832

Special Issue Editor


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Guest Editor
Department of Information Engineering, Polytechnic University of Marche, 60121 Ancona, Italy
Interests: social and complex network analysis; Internet of Things; logic programming and methods for coupling inductive and deductive reasoning; advanced algorithms for sequences comparison; bioinformatics and medical informatics applications; data mining and data science
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Special Issue Information

Dear Colleagues,

Network science and hypernetwork science have emerged as powerful disciplines that enable the study of complex systems in a wide range of domains. While traditional approaches are of paramount importance, these often fall short in capturing the intricate interconnections and dynamics of complex systems. Network science provides a holistic framework to analyze and model the relationships and interactions among components within a system, uncovering hidden patterns, emergent properties, and global behaviors.

In recent years, the field of network science has expanded its scope to include hypernetworks, which capture higher-order interactions and dependencies among elements. The study of network science and hypernetwork science is motivated by the inherent complexity and interconnectedness observed in numerous real-world systems. From social networks and biological systems to technological infrastructures and transportation networks, these systems exhibit intricate structures and dynamic behaviors that can only be fully understood through a network perspective. By employing network science and hypernetwork science methodologies, researchers can uncover fundamental principles, extract meaningful insights, and develop innovative applications.

This Special Issue aims to bring together the latest advancements, novel models, paradigms, and applications in these fields. We invite researchers from diverse disciplines to contribute original research articles as well as review papers in order to disseminate cutting-edge research for understanding and tackling complex systems.

Topics include (but are not limited to):

- Novel network and hypernetwork models;

- Dynamic and temporal network and hypernetwork models;

- Community detection and clustering;

- Centrality measures in hypernetworks;

- Resilience and robustness metrics;

- Evolutionary dynamics in hypernetworks;

- Opinion dynamics and influence propagation;

- Social (hyper)network analysis;

- Simulation of social phenomena in network and hypernetwork models;

- Network-based machine learning and data mining;

- Network science in cybersecurity and privacy;

- Network and hypernetwork science in other contexts (economics, social sciences, biology and medicine, engineering, etc.).

Dr. Francesco Cauteruccio
Guest Editor

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. Information 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 1600 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

  • network science
  • hypernetwork science
  • social network analysis
  • complex network analysis
  • network modeling

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Published Papers (3 papers)

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Research

24 pages, 3952 KiB  
Article
Confrontation of Capitalism and Socialism in Wikipedia Networks
by Leonardo Ermann and Dima L. Shepelyansky
Information 2024, 15(9), 571; https://doi.org/10.3390/info15090571 - 18 Sep 2024
Cited by 2 | Viewed by 1067
Abstract
We introduce the Ising Network Opinion Formation (INOF) model and apply it to the analysis of networks of six Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the [...] Read more.
We introduce the Ising Network Opinion Formation (INOF) model and apply it to the analysis of networks of six Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the Monte Carlo iterations in which a given spin orientation is determined by in-going links from other spins. The main consideration was the opinion confrontation between capitalism, imperialism (blue opinion) and socialism, communism (red opinion). These nodes have fixed spin/opinion orientation while other nodes achieve their steady-state opinions in the process of Monte Carlo iterations. We found that the global network opinion favors socialism, communism for all six editions. The model also determined the opinion preferences for world countries and political leaders, showing good agreement with heuristic expectations. We also present results for opinion competition between Christianity and Islam, and USA Democratic and Republican parties. We argue that the INOF approach can find numerous applications for directed complex networks. Full article
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16 pages, 1203 KiB  
Article
Defining Nodes and Edges in Other Languages in Cognitive Network Science—Moving beyond Single-Layer Networks
by Michael S. Vitevitch, Alysia E. Martinez and Riley England
Information 2024, 15(7), 401; https://doi.org/10.3390/info15070401 - 12 Jul 2024
Cited by 1 | Viewed by 1749
Abstract
Cognitive network science has increased our understanding of how the mental lexicon is structured and how that structure at the micro-, meso-, and macro-levels influences language and cognitive processes. Most of the research using this approach has used single-layer networks of English words. [...] Read more.
Cognitive network science has increased our understanding of how the mental lexicon is structured and how that structure at the micro-, meso-, and macro-levels influences language and cognitive processes. Most of the research using this approach has used single-layer networks of English words. We consider two fundamental concepts in network science—nodes and connections (or edges)—in the context of two lesser-studied languages (American Sign Language and Kaqchikel) to see if a single-layer network can model phonological similarities among words in each of those languages. The analyses of those single-layer networks revealed several differences in network architecture that may challenge the cognitive network approach. We discuss several directions for future research using different network architectures that could address these challenges and also increase our understanding of how language processing might vary across languages. Such work would also provide a common framework for research in the language sciences, despite the variation among human languages. The methodological and theoretical tools of network science may also make it easier to integrate research of various language processes, such as typical and delayed development, acquired disorders, and the interaction of phonological and semantic information. Finally, coupling the cognitive network science approach with investigations of languages other than English might further advance our understanding of cognitive processing in general. Full article
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17 pages, 2177 KiB  
Article
The Dark Threads That Weave the Web of Shame: A Network Science-Inspired Analysis of Body Shaming on Reddit
by Enrico Corradini
Information 2023, 14(8), 436; https://doi.org/10.3390/info14080436 - 2 Aug 2023
Cited by 2 | Viewed by 4584
Abstract
Deep within online forums, we often stumble across body shaming. Words like “fat” and “ugly” are tossed around, hurting those they target. But can we peel back the layers of these online communities? In this study, social network analysis is used to shine [...] Read more.
Deep within online forums, we often stumble across body shaming. Words like “fat” and “ugly” are tossed around, hurting those they target. But can we peel back the layers of these online communities? In this study, social network analysis is used to shine a light on body shaming on Reddit, a well-known online platform. This paper presents a comprehensive social network analysis of body shaming on Reddit, one of the largest online platforms. The research delves into the intricacies of body shaming by identifying key actors, communities, and patterns of behavior and communication related to body shaming. The results show how behavior and communication differ across Reddit’s various subgroups, and how user activity and the length of comments can vary. Through the application of topic modeling, the main subjects discussed in each subgroup were identified. This enables an understanding of what drives discussions about body shaming. The findings provide valuable insights into the spread and normalization of harmful behaviors and attitudes related to body shaming, which can inform the development of targeted interventions aimed at reducing this harmful behavior and promoting more positive and inclusive attitudes towards body image and weight. Full article
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