Abstract
Many applications require identifying nodes that perform similar functions in a graph. Learning latent representations that capture such structural role information about nodes has recently gained a lot of attention. A state-of-the-art algorithm, struc2vec, generates such representations for the nodes of undirected networks. However, the algorithm is unable to handle directed, weighted networks. In this paper, we present struc2vec++, a generalization of the above algorithm to such types of networks. We evaluate struc2vec++ on real and synthetic networks. We show that taking into account edge directions greatly improves performance. We compare struc2vec++ against a recently proposed algorithm. Although struc2vec++ is in most cases outperformed by the competing algorithm, experiments in a variety of different scenarios demonstrate that it is much more memory efficient and it can better capture structural roles in the presence of noise.
Giannis Nikolentzos is supported by the project “ESIGMA” (ANR-17-CE40-0028).
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Steenfatt, N., Nikolentzos, G., Vazirgiannis, M., Zhao, Q. (2019). Learning Structural Node Representations on Directed Graphs. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_11
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