Modeling Real-World Problems Using Complex Networks

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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