Abstract
Node embedding is the process of mapping a set of vertices from a graph onto a vector space. Modern deep learning embedding methods use random walks on the graph to sample relationships between vertices. These methods rely on symmetric affinities between nodes and do not translate well to directed graphs. We propose a method to learn vector embeddings of nodes in a graph as well as the parameters of an asymmetric similarity function that can be used to retain the direction of relationships in the embedding space. The effectiveness of our approach is illustrated visually by the 2D embedding of a lattice graph as well quantitatively in multiple link prediction experiments on real world datasets.
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Dernbach, S., Towsley, D. (2020). Asymmetric Node Similarity Embedding for Directed Graphs. In: Barbosa, H., Gomez-Gardenes, J., Gonçalves, B., Mangioni, G., Menezes, R., Oliveira, M. (eds) Complex Networks XI. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-40943-2_8
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