User trust relationship network link prediction method and system based on gating mechanism

Abstract

The invention discloses a user trust relationship network link prediction method and system based on a gating mechanism, and the method comprises: obtaining comment interaction data between users, andconstructing a user trust relationship network; extracting an adjacency matrix based on the user trust relationship network, and converting the adjacency matrix into a directed activation propagationadjacency matrix; calculating a symbol network reachable matrix in combination with the symbol network activation propagation adjacency matrix; processing the symbol network reachable matrix based ona gating mechanism; taking the processed reachable matrix as input of a graph convolutional network to obtain a symbol network for encoding and a network embedding result; and taking the network embedding result as a code of the symbol network, and performing similarity measurement between nodes in the network by using an inner product decoding mode to obtain a reconstructed symbol network adjacency matrix, namely a user trust relationship network link prediction result. An accurate network embedding result is obtained through the graph convolution network, the speed of user trust relationship prediction is increased, and the prediction accuracy is ensured.

Description

technical field [0001] The invention belongs to the technical field of network link prediction, and in particular relates to a user trust relationship network link prediction method and system based on a gating mechanism. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Nowadays, most research data can be represented graphically, so there is a great demand for generalized neural network models for graphical data, such as research on user credibility in comment trust networks, and user preferences in e-commerce platforms. Recommendations and avoidance of disgust, research on user similarity in major traffic platforms and recommendation strategies. The more mature methods in the research of the above fields include matrix-based symbol prediction method and network embedding method. The former excavates the potential similarity between netw...

AI Technical Summary

Problems solved by technology

However, the directed symbolic network does not have this excellent property, so it cannot learn the negative relationship in the symbolic network, resulting in a serious imbalance in the final embedding result.

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