Title: Learning Edge Representations via Low-Rank Asymmetric Projections,
Abstract: We propose a new method for embedding graphs while preserving directed edge
information. Learning such continuous-space vector representations (or
embeddings) of nodes in a graph is an important first step for using network
information (from social networks, user-item graphs, knowledge bases, etc.) in
many machine learning tasks.
Unlike previous work, we (1) explicitly model an edge as a function of node
embeddings, and we (2) propose a novel objective, the "graph likelihood", which
contrasts information from sampled random walks with non-existent edges.
Individually, both of these contributions improve the learned representations,
especially when there are memory constraints on the total size of the
embeddings. When combined, our contributions enable us to significantly improve
the state-of-the-art by learning more concise representations that better
preserve the graph structure.
We evaluate our method on a variety of link-prediction task including social
networks, collaboration networks, and protein interactions, showing that our
proposed method learn representations with error reductions of up to 76% and
55%, on directed and undirected graphs. In addition, we show that the
representations learned by our method are quite space efficient, producing
embeddings which have higher structure-preserving accuracy but are 10 times
smaller.