Message Passing
Definition
Message passing connects information of neighboring nodes in a neural network.
A computational principle in graph neural networks where nodes aggregate information from their neighbors and update their representation accordingly.
Simply explained
Each point collects data from its neighbors and adjusts its own representation accordingly.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
A node collects features from its neighbors and updates its representation accordingly.
Limits and pitfalls
- Confusing the GNN concept with arbitrary message exchange or ignoring direction, aggregation, range, and over-smoothing.
Origin & history
Message passing is also common in distributed systems; in the GNN context, message passing was formalized as a common framework in 2017.
Use in mid-sized companies
- Possible application: A node collects features from its neighbors and updates its representation accordingly.
Frequently asked questions
What does message passing mean in graph neural networks?
A computational principle in graph neural networks where nodes aggregate information from their neighbors and update their representation accordingly.
Which mistakes should be avoided?
Confusing the GNN concept with arbitrary message exchange or ignoring direction, aggregation, range, and over-smoothing.
Sources
Source checked on 10.10.2026. Editorial use cases are hypothetical; no evidence of real customer results.
Editorially reviewed on: