mlsanityData to 14 Oct 2026

Asynchronous Neural Networks for Learning in Graphs

arXiv 2205.12245 ↗First posted 24 May 2022cs.LGcited by 4

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Abstract (arXiv)

This paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs. Existing graph neural networks use the synchronous distributed computing model and aggregate their neighbors in each round, which causes problems such as oversmoothing and limits their expressiveness. On the other hand, AMP is based on the asynchronous model, where nodes react to messages of their neighbors individually. We prove that (i) AMP can simulate synchronous GNNs and that (ii) AMP can theoretically distinguish any pair of graphs. We experimentally validate AMP's expressiveness. Further, we show that AMP might be better suited to propagate messages over large distances in graphs and performs well on several graph classification benchmarks.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 48 of its 62 references that are in the map. First posted on arXiv 24 May 2022.

Builds on

48 of 62 references.

References · 48

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36 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.

  1. 2023DRew: Dynamically Rewired Message Passing with Delaycited by 82
  2. 2023Cooperative Graph Neural Networkscited by 36
  3. 2025Spectral Theory for Edge Pruning in Asynchronous Recurrent Graph Neural Networkscited by 0
  4. 2025ParaGate: Parasitic-Driven Domain Adaptation Transfer Learning for Netlist Performance Predictioncited by 0