Identifying Influential Spreaders In Complex Networks Based On Gravity Model

With the rapid development of network information technology, human society has entered the complex networks era. Many problems can be ab-stracted as the study of complex networks in the real world. And one of the important problems in the complex networks is to identify influential nodes in the networks. How to identify influential spreaders in social networks is crucial for accelerating information diffusion and curbing the spread of disease. How to effectively evaluate the nodes’importance in the networks is more and more important.This thesis first introduces the research background of the influential n-odes in the networks and some basic concepts and models of the networks. And then puts forward the gravity model, compared with other sorting methods, it is concluded that this method has certain advantages. Finally, the gravity model is used to identify the minimum k-core node’s spreading ability, and the model can effectively identify the marginal nodes in the networks. The main contributions of this thesis are summarized as follows:1. Inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. In this model, we view the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. The gravity model also has the high advantage in the resolution of the networks. We also use the classical SIR epidemic model to verify the real spreading ability of the nodes.2. Furthermore, we proposed an H index to estimate spreading influence of the minimum ks node based on the gravity model. Compared with SD in-dex and DC index, our method can quantify the marginal node influence more accurately. And with the increase of the transmission rate β, the standard de-viation of the propagation ability of the minimum k-core node in the networks becomes much larger, and the divergence degree of the dissemination ability is increased.3. Finally we conclude the work we have done and present outlook of further researches in this field.