Scientists from Saratov have created a complex model that describes the evolution of various networks. This will help to better understand how systems such as social networks, web page networks, or transport networks change over time, the press service of the Ministry of Education and Science of Russia reported.
Scientists at Saratov State National Research University named after N. G. Chernyshevsky (SGU) have taken an important step in understanding the mechanisms of complex network evolution. They have developed a comprehensive model that takes into account not only the growth, but also the reduction of network structures. This discovery provides a deeper understanding of how systems such as social networks, web page networks, or transport networks change over time.
The ministry reported that in the past two decades, researchers have focused on studying network growth, using the principle of preferential attachment. However, the process of network reduction — the disappearance of nodes and connections — remained poorly understood. The new model combines three main mechanisms: growth dynamics — adding new nodes to the network, reduction processes — merging nodes, and triadic closure — forming new connections between neighbors of merged nodes.
In our work, we considered how such networks not only grow, but also shrink, and proposed a model that takes both processes into account simultaneously. This allowed us to better understand how complex systems actually evolve, and in the future will help to apply this knowledge, for example, to predict the development of social networks or analyze biological interactions.
In the course of research, experts found that the new model not only reproduces the known characteristics of real networks, but also demonstrates new trends. Firstly, networks demonstrate saturation of the number of connections and the clustering coefficient. Secondly, the use of triadic closure helps to increase resistance to attacks. Thirdly, it becomes possible to model both expanding and shrinking systems by adjusting the parameters of network growth and reduction.
The Ministry of Science and Higher Education reported that the research conducted by scientists from Saratov can be useful in various fields. In particular, it can be used to analyze network security and prevent targeted attacks.
The created model of network evolution helps to understand how connections between users disappear and prevent a decrease in activity. In addition, the study provides new methods for studying the dynamics of citation and the relevance of scientific knowledge.
Earlier, scientists from Novosibirsk State University developed unique materials that can become the basis for creating future advanced memory devices. These devices will significantly surpass flash memory in terms of the number of rewrite cycles, memory capacity, and speed.
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