@inproceedings{6fac219b32f548678c699c5230ca9df9,
title = "Learning structural node representations on directed graphs",
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.",
keywords = "Node embeddings, Role discovery, Structural identity",
author = "Niklas Steenfatt and Giannis Nikolentzos and Michalis Vazirgiannis and Qiang Zhao",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 ; Conference date: 11-12-2018 Through 13-12-2018",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-05414-4\_11",
language = "English",
isbn = "9783030054137",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "132--144",
editor = "Aiello, \{Luca Maria\} and Hocine Cherifi and Pietro Li{\'o} and Rocha, \{Luis M.\} and Chantal Cherifi and Renaud Lambiotte",
booktitle = "Complex Networks and Their Applications VII - Volume 2 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018",
}