Development and Evaluation of Embedding Methods for Graphs with Multi Attributes

Miyu Fujii, David Taingngin, Keiichiro Yamamura, Nozomi Hata, Hiroki Kai, Ryuji Noda, Hiroki Ishikura, Tatsuru Higurashi, Katsuki Fujisawa

Abstract: Graph embedding is the process of obtaining a vector representation of graph nodes. The representation obtained by graph embedding is highly versatile. It can be used for various tasks, such as recommendation and clustering tasks. However, there are only a few methods that incorporate attributes indicating node characteristics, such as user gender, age, or product category, into graph embedding. Therefore, we hypothesize that nodes with the same attribute are often connected to the same node. Consequently, we propose two methods for graph embedding, parallel and serial, that use metric learning to reflect attribute data in node features. The proposed method can be applied to any graph embedding and metric learning method, and thus can also be applied to many new methods yet to be developed. Numerical experimental results show that the proposed method using node attributes is superior to the existing methods in both AUROC and accuracy.