Expressive Power of Temporal Message Passing

Przemyslaw Walega will be presenting Expressive Power of Temporal Message Passing (Wałęga & Rawson, 2024)

Abstract

Graph neural networks (GNNs) have recently been adapted to temporal settings, often employing temporal versions of the message-passing mechanism known from GNNs. We divide temporal message passing mechanisms from literature into two main types: global and local, and establish Weisfeiler-Leman characterisations for both. This allows us to formally analyse expressive power of temporal message-passing models. We show that global and local temporal message-passing mechanisms have incomparable expressive power when applied to arbitrary temporal graphs. However, the local mechanism is strictly more expressive than the global mechanism when applied to colour-persistent temporal graphs, whose node colours are initially the same in all time points. Our theoretical findings are supported by experimental evidence, underlining practical implications of our analysis.

Updates

Since this talk was presented it the work was published at AAAI (Wałęga & Rawson, 2025)

References

  1. Expressive Power of Temporal Message Passing
    Przemysław Andrzej Wałęga and Michael Rawson
    2024
  2. Expressive Power of Temporal Message Passing
    Przemysław Andrzej Wałęga and Michael Rawson
    Proceedings of the AAAI Conference on Artificial Intelligence, Apr 2025



Enjoy Reading This Article?

Here are some more articles you might like to read next:

  • Deep Learning is Not So Mysterious or Different
  • The Biophysical Principles Underlying Computation in Neural Substrates
  • Approaching Deep Learning through the Spectral Dynamics of Weights
  • Explaining Transformers Using Model-Based Stochastic Signal Processing
  • NeurIPS 2024 Recap