Automatic Metapath Generating In Heterogeneous Graphs for Representation Learning

Abstract

In this article, the problem of learning representation in heterogeneous graphs is investigated. Due to the presence of different types of nodes and edges in this type of graphs, there are unique challenges that limit the possibility of using conventional graph representation techniques. The way of random walk in this type of graphs is different and they need a walking scheme or metapath to find the path. Specifying this scheme is one of the challenges of learning representation in heterogeneous graphs. In this article, an algorithm has been introduced that finds all possible metapath schema by taking an heterogeneous graph and finds the best metapath scheme by specifying the correct schema and checking them. Various experiments show that with a small sampling of the network in the form of short length, the most suitable scheme can be found automatically and it is shown that by changing the sampling size, the selected scheme is the best scheme and in terms of time only Runs in 0.007% of the time using long random walks.