GSWL test bounds the power of geometry-aware simplicial message passing, can be matched by such networks on finite families, and together with the Euler Characteristic Transform yields a complete geometric expressivity characterization.
Geometrically equivariant graph neural networks: A survey
6 Pith papers cite this work, alongside 33 external citations. Polarity classification is still indexing.
6
Pith papers citing it
33
external citations · external index
verdicts
UNVERDICTED 6representative citing papers
FRAMES training with minimal temporal information from MD trajectory pairs improves energy and force prediction accuracy over Equiformer on MD17 and ISO17 benchmarks.
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
GSNO uses position-dependent spherical Green's functions to create flexible neural operators that adapt to non-equivariant systems on spheres while keeping spectral efficiency and grid invariance.
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
citing papers explorer
-
Geometry-Aware Simplicial Message Passing
GSWL test bounds the power of geometry-aware simplicial message passing, can be matched by such networks on finite families, and together with the Euler Characteristic Transform yields a complete geometric expressivity characterization.
-
Improving Molecular Force Fields with Minimal Temporal Information
FRAMES training with minimal temporal information from MD trajectory pairs improves energy and force prediction accuracy over Equiformer on MD17 and ISO17 benchmarks.
-
Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
-
Generalized Spherical Neural Operators: Green's Function Formulation
GSNO uses position-dependent spherical Green's functions to create flexible neural operators that adapt to non-equivariant systems on spheres while keeping spectral efficiency and grid invariance.
-
Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
-
Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.