Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding

01/05/2021
by   Giorgio Valentini, et al.
0

We introduce a set of algorithms (Het-node2vec) that extend the original node2vec node-neighborhood sampling method to heterogeneous multigraphs, i.e. networks characterized by multiple types of nodes and edges. The resulting random walk samples capture both the structural characteristics of the graph and the semantics of the different types of nodes and edges. The proposed algorithms can focus their attention on specific node or edge types, allowing accurate representations also for underrepresented types of nodes/edges that are of interest for the prediction problem under investigation. These rich and well-focused representations can boost unsupervised and supervised learning on heterogeneous graphs.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset