Learning Structural Node Representations on Directed Graphs

  • Niklas Steenfatt
  • Giannis NikolentzosEmail author
  • Michalis Vazirgiannis
  • Qiang Zhao
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)


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.


Role discovery Node embeddings Structural identity 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Niklas Steenfatt
    • 1
  • Giannis Nikolentzos
    • 1
    Email author
  • Michalis Vazirgiannis
    • 1
    • 2
  • Qiang Zhao
    • 3
  1. 1.École PolytechniquePalaiseauFrance
  2. 2.Athens University of Economics and BusinessAthensGreece
  3. 3.TencentShenzhenChina

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