Dynamic link prediction method based on space-time attention deep model

Abstract

The invention discloses a dynamic link prediction method for a space-time attention deep model, and the method comprises the following steps: taking an adjacent matrix A corresponding to a dynamic network as an input, and the dynamic network comprises a social network, a communication network, a scientific cooperation network or a social security network; extracting a hidden layer vector {ht-T,..., ht-1} from the hidden layer vectors {ht-T,..., ht-1} by means of an LSTM-attention model, calculating a context vector at according to the hidden layer vectors {ht-T,..., ht-1} at T moments, and inputting the context vector at the T moments into a decoder as a space-time feature vector; and decoding the input time feature vector at by adopting a decoder, and outputting a probability matrix whichis obtained by decoding and is used for representing whether a link exists between the nodes or not, thereby realizing the prediction of the dynamic link. According to the dynamic link prediction method, link prediction of the end-to-end dynamic network is realized by extracting the spatial and temporal characteristics of the dynamic network.

Description

technical field [0001] The invention belongs to the field of network science, and in particular relates to a dynamic link prediction method based on a spatio-temporal attention depth model. Background technique [0002] Dynamic link prediction of complex networks is widely used in various fields, including social networks, economics, biology, and industrial systems. The structure of most real networks evolves over time (nodes or edges are added and deleted over time), and link prediction for such networks is called dynamic network link prediction. Dynamic network link prediction has been widely used in various real-world networks, including predicting friend relationships in social networks, predicting future communication relationships in communication networks, predicting future colleague relationships in scientific collaboration networks, and locating criminals in social security networks. Predict evolutionary patterns in the timing of crime, disease transmission, protei...

AI Technical Summary

Problems solved by technology

[0009] Most real-world network data do not have a regular spatial structure, resulting in the convolutional neural network widely used in the image field cannot handle these network data

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