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evolve2vec: Learning Network Representations Using Temporal Unfolding

  • Nikolaos Bastas
  • Theodoros SemertzidisEmail author
  • Apostolos Axenopoulos
  • Petros Daras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

Abstract

In the past few years, various methods have been developed that attempt to embed graph nodes (e.g. users that interact through a social platform) onto low-dimensional vector spaces, exploiting the relationships (commonly displayed as edges) among them. The extracted vector representations of the graph nodes are then used to effectively solve machine learning tasks such as node classification or link prediction. These methods, however, focus on the static properties of the underlying networks, neglecting the temporal unfolding of those relationships. This affects the quality of representations, since the edges don’t encode the response times (i.e. speed) of the users’ (i.e. nodes) interactions. To overcome this limitation, we propose an unsupervised method that relies on temporal random walks unfolding at the same timescale as the evolution of the underlying dataset. We demonstrate its superiority against state-of-the-art techniques on the tasks of hidden link prediction and future link forecast. Moreover, by interpolating between the fully static and fully temporal setting, we show that the incorporation of topological information of past interactions can further increase our method efficiency.

Keywords

Temporal random walks Representation learning Link prediction Link forecast 

Notes

Acknowledgments

The work presented in this paper was supported by the European Commission under contract H2020-700381 ASGARD.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nikolaos Bastas
    • 1
  • Theodoros Semertzidis
    • 1
    Email author
  • Apostolos Axenopoulos
    • 1
  • Petros Daras
    • 1
  1. 1.Centre for Research and Technology, Hellas (CERTH)ThessalonikiGreece

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