JONNEE: Joint Network Nodes and Edges Embedding

Abstract

Recently, graph embedding models significantly improved the quality of graph machine learning tasks, such as node classification and link prediction. In this work, we propose a model called JONNEE (JOint Network Nodes and Edges Embedding), which learns node and edge embeddings under self-supervision via joint constraints in a given graph and its edge-to-vertex dual representation as a Line graph. The model uses two graph autoencoders with additional structural feature engineering and several regularization techniques to train for an adjacency matrix reconstruction task in an unsupervised setting. Experimental results show that our model performs on par with state-of-the-art undirected attribute graph embedding models and requires less number of epochs to achieve the same quality due to Line graph selfsupervision under a unified embedding framework.