What is recorded here?
Uses invariant distance-conditioned scalar messages and radial coordinate updates to preserve Euclidean rotation, reflection and translation equivariance without computing higher-order spherical-harmonic features.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Equivariance is structural for the specified updates and invariant input features, not universal expressivity across all tensor representations, exact physical conservation or arbitrary graph-inference scalability. General architecture and artificial particle dynamics only; bio applications omitted.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Victor Garcia Satorras; Emiel Hoogeboom; Max Welling
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
E(n)-equivariant graph neural networks
Uses invariant distance-conditioned scalar messages and radial coordinate updates to preserve Euclidean rotation, reflection and translation equivariance without computing higher-order spherical-harmonic features.
Scientific scope
Equivariance is structural for the specified updates and invariant input features, not universal expressivity across all tensor representations, exact physical conservation or arbitrary graph-inference scalability. General architecture and artificial particle dynamics only; bio applications omitted.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Victor Garcia Satorras; Emiel Hoogeboom; Max Welling
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.