Node Representation Learning for Directed Graphs

We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. In order to achieve this, we propose a novel alternating random walk strategy to generate training samples from the directed graph while preserving the role information. These samples are then trained using Skip-Gram with Negative Sampling (SGNS) with nodes retaining their source/target semantics. We conduct extensive experimental evaluation to showcase our effectiveness on several real-world datasets on link prediction, multi-label classification and graph reconstruction tasks. We show that the embeddings from our approach are indeed robust, generalizable and well performing across multiple kinds of tasks and networks. We show that we consistently outperform all random-walk based neural embedding methods for link prediction and graph reconstruction tasks. In addition to providing a theoretical interpretation of our method we also show that we are more considerably robust than the other directed graph approaches.