Link Prediction Based on Orbit Counting and Graph Auto-Encoder
IEEE ACCESS(2020)
Abstract
Link prediction aims to predict the missing edge or the edge that may be generated in the future. The key to link prediction is to obtain the characteristic information with strong representation for nodes. In this work, aiming at the problem that the existing link prediction methods fail to take full account of the high-order connection mode of nodes, we present a new representation learning-based approach called OC-GAE (Orbit Counting and Graph Auto-Encoder) that considers rich subgraph structure around nodes. Firstly, the number of orbits on subgraphs is calculated as the high-order structural features of the nodes; then, the number of orbits is used as the input of Graph Auto-Encoder to learn the efficient representation of the nodes; finally, the network adjacency matrix is reconstructed by the learned representation to realize the link prediction. By comparing with five classical link prediction methods and two mainstream network representation learning methods on four real network datasets, the effectiveness of the proposed method and the prediction accuracy are proved to be optimal in general.
MoreTranslated text
Key words
Orbits,Task analysis,Licenses,Predictive models,Feature extraction,Prediction algorithms,Network topology,Graph auto-encoder,link prediction,orbit counting,representation learning,subgraph
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined