A New Ranking Technique to Enhance the Infection Size in Complex Networks

Detecting the spreaders/sources in complex networks is an essential manner to understand the dynamics of the information spreading process. Consider the k-Shell centrality metric, which is taken into account the structural position of a node within the network, a more effective metric in picking the node which has more ability on spreading the infection compared to other centrality metrics such the degree, between and closeness.  However, the K-Shell method suffers from some boundaries, it gives the same K-Shell index to a lot of the nodes, and it uses only one indicator to rank the nodes. A new technique is proposed in this research to develop the K-Shell metric by using the degree of the node, and a coreness of its rounding friends to estimate the ability of the node in spreading the infection within the network. The experimental results, which were done on four types of real and synthetic networks, and using an epidemic propagation model SIR, demonstrate that the suggested technique can measure the node effect more precisely and offer a unique ordering group than other centrality measures

Identifying influential nodes with centrality indices combinations using symbolic regressions

Numerous strategies for determining the most influential nodes in a connected network have been developed. The use of centrality indices in a network allows the identification of the most important nodes in the network. Specific indices, on the other hand, cannot search for a network's entire meaning because they are only interested in a single attribute. Researchers frequently overlook an index's characteristics in favour of focusing on its application. The purpose of this research is to integrate selected centrality indices classified by their various properties. A symbolic regression approach was used to find meaningful mathematical expressions for this combination of indices. When the efficacy of the combined indices is compared to other methods, the combined indices react similarly and outperform the previous method. Using this adaptive technique, network researchers can now identify the most influential network nodes

Initial Value Dependence of Urban Population''s Growth-Rate Distribution and the Long-Term Growth

This paper uses census municipal population data for the United States, Italy, and Spain to analyze the statistical properties of their 10-year growth (short-term property). As a result, it was confirmed that the smaller the initial urban population is, the greater the probability that the urban population will decrease and that the probability that the urban population will increase does not depend on the initial urban population. We also observed the statistical properties of long-term growth of urban populations in each country over 100 years. Specifically, we identified the following properties by observing the geometric mean of logarithmically equal sized bins of the oldest urban population in the data used in the analysis. (1) The average urban population increases or decreases exponentially with time. (2) The smaller the initial average urban population, the smaller the exponent, which can be negative in Italy and Spain. (3) When the average urban population is large, exponential growth may stop. We showed that these long-term properties are derived from the short-term property by random sampling simulations from real data