Multi-scale link prediction method and system based on hierarchical link mode

Abstract

The invention discloses a multi-scale link prediction method and system based on a hierarchical link mode, and the method comprises the steps: constructing K k-order link graphs based on a visible graph of a real target network; vector representations of the nodes on different scales are obtained according to the k-order link graph, and the final representation of each node is obtained by splicing the vectors; constructing a dichotomy training set; and establishing a dichotomy model for multi-scale link prediction. The system comprises a link graph module used for constructing K k-order link graphs based on a visible graph of a real target network; the node representation module is used for obtaining vector representations and final representations of nodes on link diagrams with different scales; the classification module is used for constructing a dichotomy training set; and the prediction module is used for establishing a dichotomy model and performing multi-scale link prediction. According to the method, the high-order link of the network can be effectively captured, and the problem that the network is too sparse is better solved; the prejudice of artificially defining edge embedding binary operators is overcome; and the prediction performance is improved.

Description

technical field [0001] The present application belongs to the technical field of network topology, and in particular relates to a multi-scale link prediction method and system based on a hierarchical link pattern. Background technique [0002] The purpose of link prediction is to predict the probability that two nodes in the network are connected, which is crucial for many applications, such as personalized product recommendation in order to improve user experience and business sales in e-commerce networks; online social networking In the network, friend recommendation based on the user's relationship topology; in the biological protein interaction network, it can assist in predicting the interaction between proteins; in the transportation network, according to the existing transportation mode connection between cities, add new traffic line connection. [0003] The development of link prediction can be roughly divided into two stages: in the first stage, a large number of h...

AI Technical Summary

Problems solved by technology

However, the method represented by matrix decomposition often brings excessive memory overhead and computational complexity due to matrix storage and calculation.

However, the graph neural network-based method represented by SEAL needs to construct a subgraph for each pair of nodes for binary classification, which will bring the following disadvantages: 1) The construction process of the subgraph is often too time-consuming; link, you need to construct its corresponding subgraph

[0004] However, the method based on random walk also has obvious defects: one is that it cannot capture the hierarchical structure in the network, and the other is that the graph embedding method represented by Node2Vec constructs manual features of edges in an artificially defined way for binary classification. Lack of sufficient adaptability to data

Images