Undirected network edge-connectivity weight prediction method based on node similarity

Abstract

The invention relates to an undirected network edge-connectivity weight prediction method based on node similarity. The undirected network edge-connectivity weight prediction method comprises following steps of 1) establishing an undirected network diagram in dependence on an existing data set including nodes and the edge-connectivity weight; 2) respectively calculating following three kinds of similarity indexes including the local similarity index, the global similarity index and the semi-local similarity index in the network diagram in step 1); and 3) predicting the edge-connectivity weight in the test set in dependence on the three kinds of similarity indexes calculated in the step 2) and by means of a multivariate linear regression model, and then examining the model mean performances including the Pearson coefficient and the root-mean-square value by means of a tenfold crossing verification method. According to the invention, by means of the node similarity, the missing edge-connectivity weight is predicted through the multivariate linear regression model, the model is simple, and the prediction result is good.

Description

technical field [0001] The invention relates to the fields of link prediction and data mining, in particular to a method for predicting connection weights based on network node similarity. Background technique [0002] In reality, many systems can be abstracted as complex network models. Individual objects in the system are abstracted as nodes, and the relationship between individuals is abstracted as edges, such as social networks, protein interaction networks, and power networks. Among them, the network edge, as a bridge connecting individual objects, plays an important role in revealing the network structure. In reality, the edges of many networks have weights, and these edge weights have clear physical meanings. Due to various reasons, some network edge weights may be missing, especially when the missing weights contain important network structure information, the prediction of these weights is very critical. Contents of the invention [0003] In order to overcome th...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the shortcomings of poor model prediction results caused by the lack of edge weights in the existing network, the present invention uses the similarity of network nodes to predict the missing edge weights using a multiple linear regression model, and provides a model with better prediction results. The edge weight prediction method based on the similarity of undirected network nodes, what the present invention relates to is the weight prediction method of undirected network

Images