Modeling Real-World Problems Using Complex Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 505

Special Issue Editors


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Guest Editor
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Interests: complex networks; metaheuristics; computational biology; network sciences and social networks
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Guest Editor
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Interests: data mining; big data; optimization

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Guest Editor
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Interests: algorithms; evolutionary algorithms; combinatorial optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Interests: computing in mathematics; natural science; engineering and medicine

Special Issue Information

Dear Colleagues,

The knowledge of a complex network of a given system provides valuable information into the system itself and its behavior. Real-world problems can be represented as a network with non-trivial topologies, where nodes represent real-world entities and the links represent the interactions between them. This type of non-trivial network, called a Complex Network, provides important information about the behavior of a complex system. By studying the iterations between the entities, it is possible to understand the functioning of the system by predicting its effects in response to one or more causes. However, the topology of the complex network associated with a real-world problem is unknown a priori, and the interactions between entities are almost always unknown. In nature, the only way to understand the relationship between entities is to observe them in the real world and, based on this information, understand how the system works. This procedure is known as the inverse problem or reverse engineering, which involves exploiting all the information collected from observation to deduce a mathematical model that represents a system. The application of reverse engineering problems has numerous applications in various fields. One example is its use in the field of genetics. In this case, starting from the observation of how gene expression changes over time, a reverse engineering approach allows for the discovery of the real interactions between genes, which is very useful for discovering diseases and providing a biological way of understanding our DNA and how it works. In recent years, the application of complex networks has also found its way into the application of machine learning. Complex networks are a perfect fit for representing knowledge, as they provide an organized representation of the information that a machine learning algorithm learns during training. This information is then used to make predictions or to discover new patterns in the data. Therefore, complex networks are powerful tools used in the field of modeling for modeling the entities of real-world systems. One specific application of complex networks is the modeling of community and human social behavior. Other applications are in fields ranging from social network analysis to healthcare and transportation systems. Complex networks have also been utilized in recent fields such as smart cities and smart grids, which aim to promote sustainable energy and improve the well-being of citizens. In recent years, numerous investments have been made in the field of renewable energy, particularly in the realm of cities and industries. Complex networks could be a crucial tool in modeling these complex relationships and enhancing sustainability.

Dr. Mario F. Pavone
Dr. Claudia Cavallaro
Prof. Dr. Vincenzo Cutello
Guest Editors

Francesco Zito
Guest Editor Assistant

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Keywords

  • models of complex networks
  • dynamics and evolution patterns of complex networks
  • link prediction
  • biological networks
  • synchronization in networks
  • genetical expression
  • knowledge and information networks
  • influential node detection
  • community structure
  • social network analysis
  • spreading and propagation on complex networks

Published Papers (1 paper)

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Research

25 pages, 6916 KiB  
Article
Spatial Constraints on Economic Interactions: A Complexity Approach to the Japanese Inter-Firm Trade Network
by Eduardo Viegas, Orr Levy, Shlomo Havlin, Hideki Takayasu and Misako Takayasu
Mathematics 2024, 12(8), 1244; https://doi.org/10.3390/math12081244 - 19 Apr 2024
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Abstract
The trade distance is an important constraining factor underpinning the emergence of social and economic interactions of complex systems. However, agent-based studies supported by the granular analysis of distances are limited. Here, we present a complexity method that places the actual geographical locations [...] Read more.
The trade distance is an important constraining factor underpinning the emergence of social and economic interactions of complex systems. However, agent-based studies supported by the granular analysis of distances are limited. Here, we present a complexity method that places the actual geographical locations of individual firms in Japan at the epicentre of our research. By combining methods derived from network science together with information theory measures, and by using a comprehensive dataset of Japanese inter-firm business transactions, we evaluate the effects of spatial features on the structural patterns of the economy. We find that the normalised probability distributions of the distances between interacting firms obey a power law like decay concomitant with the sizes of firms and regions. Furthermore, small firms would reach large distances to become customers of large firms, while trading between either only small firms or only large firms tends to be at smaller distances. Furthermore, a time evolution analysis suggests a reduction in the overall average trading distances in last 20 years. Lastly, our analysis concerning the trading dynamics among prefectures indicates that the preference to trade with neighbouring prefectures tends to be more pronounced at rural regions as opposed to the larger central conurbations. Full article
(This article belongs to the Special Issue Modeling Real-World Problems Using Complex Networks)
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