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TemporalNode2vec: Temporal Node Embedding in Temporal Networks

  • Mounir HaddadEmail author
  • Cécile Bothorel
  • Philippe Lenca
  • Dominique Bedart
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

Abstract

The goal of graph embedding is to learn a representation of graphs vertices in a latent low-dimensional space in order to encode the structural information that lies in graphs. While real-world networks evolve over time, the majority of research focuses on static networks, ignoring local and global evolution patterns. A simplistic approach consists of learning nodes embeddings independently for each time step. This can cause unstable and inefficient representations over time.

We present a novel dynamic graph embedding approach that learns continuous time-aware node representations. Overall, we demonstrate that our method improves node classification tasks comparing to previous static and dynamic approaches as it achieves up to 14% gain regarding to the F1 score metric. We also prove that our model is more data-efficient than several baseline methods, as it affords to achieve good performances with a limited number of vertex representation features.

Keywords

Dynamic network embeddings Graph representation learning Latent representations 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mounir Haddad
    • 1
    • 2
    Email author
  • Cécile Bothorel
    • 1
  • Philippe Lenca
    • 1
  • Dominique Bedart
    • 2
  1. 1.IMT Atlantique, LUSSI Department, UMR Lab-sticcBrest Cedex 3France
  2. 2.DSI Global ServicesPlessis-RobinsonFrance

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