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Related Experiment Video

Updated: Sep 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Time-varying graph representation learning via higher-order skip-gram with negative sampling.

Simone Piaggesi1,2, André Panisson3

  • 1Alma Mater Studiorum University of Bologna, Bologna, Italy.

EPJ Data Science
|June 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces higher-order skip-gram with negative sampling (HOSGNS) for dynamic graph representation learning. HOSGNS effectively models temporal network changes and improves downstream tasks like disease spread prediction.

Keywords:
Representation learningSpreading processesTemporal link predictionTime-varying graphs

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Area of Science:

  • Graph representation learning
  • Machine learning for dynamic networks
  • Tensor factorization

Background:

  • Real-world networks are dynamic, with changing interactions over time.
  • Existing representation learning models often struggle with temporal network dynamics.
  • Need for methods that can effectively capture time-varying graph structures.

Purpose of the Study:

  • Generalize skip-gram embedding for time-varying graphs.
  • Develop a method for implicit tensor factorization of dynamic networks.
  • Improve node and time disentanglement in graph embeddings.

Main Methods:

  • Generalizing the skip-gram embedding approach.
  • Applying implicit tensor factorization to time-varying graph representations.
  • Introducing higher-order skip-gram with negative sampling (HOSGNS).

Main Results:

  • HOSGNS effectively disentangles node and time roles with fewer parameters.
  • Learned representations outperform state-of-the-art methods on network reconstruction.
  • Accurate prediction of dynamical processes like disease spreading.

Conclusions:

  • HOSGNS offers an efficient method for dynamic graph representation learning.
  • The approach has potential for estimating contagion risk from contact tracing data.
  • Provides early risk awareness for public health applications.