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Related Experiment Video

Updated: Dec 23, 2025

Decoding Natural Behavior from Neuroethological Embedding
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weg2vec: Event embedding for temporal networks.

Maddalena Torricelli1,2, Márton Karsai1,3,4, Laetitia Gauvin5

  • 1ISI Foundation, Turin, Italy.

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|April 30, 2020
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Summary

We introduce weg2vec, a novel temporal network embedding method. It captures event similarities to represent dynamic networks and predict spreading processes.

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Area of Science:

  • Network science
  • Data science
  • Machine learning

Background:

  • Conventional network embedding methods struggle with dynamic, temporal networks.
  • Existing models often overlook temporal dependencies and event-specific information.

Purpose of the Study:

  • To develop a new network embedding technique for temporal networks.
  • To capture both temporal and structural similarities of events.
  • To enable prediction of processes on dynamic networks.

Main Methods:

  • Proposed weg2vec, an event embedding method for temporal networks.
  • Learned low-dimensional representations based on event temporal and structural similarities.
  • Applied the learned embeddings to analyze latent network structures.

Main Results:

  • weg2vec successfully captures latent structures in temporal networks.
  • Identified similarities between events involving different nodes and times.
  • Demonstrated ability to predict outcomes of spreading processes.

Conclusions:

  • weg2vec offers an effective approach for analyzing temporal networks.
  • The method enhances understanding of dynamic systems and their evolution.
  • Provides a foundation for predicting complex network phenomena.