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weg2vec: Event embedding for temporal networks

Maddalena Torricelli 1 Márton Karsai 2 Laetitia Gauvin 1
2 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
Abstract : Network embedding techniques are powerful to capture structural regularities in networks and to identify similarities between their local fabrics. However, conventional network embedding models are developed for static structures, commonly consider nodes only and they are seriously challenged when the network is varying in time. Temporal networks may provide an advantage in the description of real systems, but they code more complex information, which could be effectively represented only by a handful of methods so far. Here, we propose a new method of event embedding of temporal networks, called weg2vec, which builds on temporal and structural similarities of events to learn a low dimensional representation of a temporal network. This projection successfully captures latent structures and similarities between events involving different nodes at different times and provides ways to predict the final outcome of spreading processes unfolding on the temporal structure.
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Preprints, Working Papers, ...
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https://hal.inria.fr/hal-02388409
Contributor : Márton Karsai <>
Submitted on : Sunday, December 1, 2019 - 8:38:50 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:31 PM

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  • HAL Id : hal-02388409, version 1
  • ARXIV : 1911.02425

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Maddalena Torricelli, Márton Karsai, Laetitia Gauvin. weg2vec: Event embedding for temporal networks. 2019. ⟨hal-02388409⟩

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