A link prediction method based on common neighbor node resource allocation and naive bayesian

Abstract

A link prediction method base on common neighbor node resource allocation and naive Bayesian is disclosed, By establishing network model G, any two unconnected nodes x and y and the common neighbor nodes of node x and y in network G are selected to calculate the distribution value of node x and node y under the action of the common neighbor nodes. Secondly, the connection attribute function of thecommon neighbor node in step S1 is obtained by using the naive Bayesian method, and the role difference of the common neighbor node is distinguished by the connection attribute function; Finally, thefinal similarity value of any two unconnected pairs of nodes in the network G is calculated by combining the assignment value between the pairs of nodes to be predicted and the connection attribute function of the common neighboring nodes, and the network link prediction is carried out according to the final similarity value of the pairs of nodes to be predicted. The invention utilizes the naiveBayesian method to supplement the attribute differences between different nodes, so that the link prediction accuracy can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of network science and technology and network link prediction, in particular to a link prediction method based on common adjacent node resource allocation and naive Bayesian. Background technique [0002] The rapid development of cities has formed a variety of complex networks around us, such as social relationship networks, economic networks, transportation networks, power networks, etc. The increasingly networked society requires us to understand all kinds of artificial and natural complexities. A better understanding of network behavior. Network science provides us with a new perspective and a new method for studying complex networks. With the increasing development and popularization of network science, people's understanding of complex networks is getting deeper and clearer. Link prediction is an important branch of network science. It mainly studies two aspects: on the one hand, it predicts some links...

AI Technical Summary

Problems solved by technology

However, the information investigated by these algorithms is too limited, the extraction and mining of the network structure are not enough, the problem of resource allocation of common neighbor nodes is not fully explored, the important role of common neighbor nodes in two unconnected nodes is not deeply explored, and there is no Considering the different effects of different attributes of the nodes themselves on the generation of links, it is impossible to effectively distinguish the different effects of common adjacent nodes on unconnected nodes on the connection.

Traditional neighbor-based methods do not have high prediction accuracy in actual networks

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