Papers › Approximately Equivariant Graph Networks

Approximately Equivariant Graph Networks

21 Aug 2023NeurIPS 2023 11arXiv:2308.10436archive 2025-07-28

Ningyuan Huang, Ron Levie, Soledad Villar

Graph neural networks (GNNs) are commonly described as being permutation equivariant with respect to node relabeling in the graph. This symmetry of GNNs is often compared to the translation equivariance of Euclidean convolution neural networks (CNNs). However, these two symmetries are fundamentally different: The translation equivariance of CNNs corresponds to symmetries of the fixed domain acting on the image signals (sometimes known as active symmetries), whereas in GNNs any permutation acts on both the graph signals and the graph domain (sometimes described as passive symmetries). In this work, we focus on the active symmetries of GNNs, by considering a learning setting where signals are supported on a fixed graph. In this case, the natural symmetries of GNNs are the automorphisms of the graph. Since real-world graphs tend to be asymmetric, we relax the notion of symmetries by formalizing approximate symmetries via graph coarsening. We present a bias-variance formula that quantifies the tradeoff between the loss in expressivity and the gain in the regularity of the learned estimator, depending on the chosen symmetry group. To illustrate our approach, we conduct extensive experiments on image inpainting, traffic flow prediction, and human pose estimation with different choices of symmetries. We show theoretically and empirically that the best generalization performance can be achieved by choosing a suitably larger group than the graph automorphism, but smaller than the permutation group.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2308.10436")

Code

Syntology Ran 9 of 14 code samples harvested from 1 repository linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · violated contract; 3 ran · our draft was wrong; 5 ran with no contract checked.

By repository: official repository: 14 samples from 1 repository, 9 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

nhuang37/approx_equivariant_graph_nets officialmentioned in paperpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

14 samples harvested; 9 ran; 0 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · violated contract
3ran · our draft was wrong
5ran
5unverified

Licence: 14 of the 14 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from nhuang37/approx_equivariant_graph_nets. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

adj_mx_from_edges nhuang37/approx_equivariant_graph_nets/Human_Pose_Est/common/graph_utils.py official repository ran · our draft was wrong no licence file found · pointer only · 267c4a7846f1eab2 · report
calculate_normalized_laplacian nhuang37/approx_equivariant_graph_nets/DCRNN_Pytorch/lib/utils.py official repository ran · our draft was wrong no licence file found · pointer only · e7b2e6a60daa42ca · report
calculate_random_walk_matrix nhuang37/approx_equivariant_graph_nets/DCRNN_Pytorch/lib/utils.py official repository ran no licence file found · pointer only · 3beffe79b677036e · report
calculate_reverse_random_walk_matrix nhuang37/approx_equivariant_graph_nets/DCRNN_Pytorch/lib/utils.py official repository ran no licence file found · pointer only · 56ec469b6198b701 · report
indexer nhuang37/approx_equivariant_graph_nets/Human_Pose_Est/models/sem_graph_conv_autG.py official repository ran no licence file found · pointer only · 4f0ac7ede58734c3 · report
mpjpe nhuang37/approx_equivariant_graph_nets/Human_Pose_Est/common/loss.py official repository ran fingerprinted no licence file found · pointer only · 3afb541a8147d123 · report
normalize nhuang37/approx_equivariant_graph_nets/Human_Pose_Est/common/graph_utils.py official repository ran · violated contract no licence file found · pointer only · aa9c29936dee40a7 · report
p_mpjpe nhuang37/approx_equivariant_graph_nets/Human_Pose_Est/common/loss.py official repository ran no licence file found · pointer only · 29d56deadc8fcd3c · report
sparse_mx_to_torch_sparse_tensor nhuang37/approx_equivariant_graph_nets/Human_Pose_Est/common/graph_utils.py official repository ran · our draft was wrong no licence file found · pointer only · bdc1f1dad6d978e7 · report
generate_graph_seq2seq_io_data nhuang37/approx_equivariant_graph_nets/DCRNN_Pytorch/generate_training_data_t3.py official repository unverified no licence file found · pointer only · fde1716c2510a3a6 · report
masked_mae_tf nhuang37/approx_equivariant_graph_nets/DCRNN_Pytorch/lib/metrics.py official repository unverified no licence file found · pointer only · 73560f28f2daab27 · report
masked_mse_tf nhuang37/approx_equivariant_graph_nets/DCRNN_Pytorch/lib/metrics.py official repository unverified no licence file found · pointer only · 22a732ec40427724 · report
masked_rmse_tf nhuang37/approx_equivariant_graph_nets/DCRNN_Pytorch/lib/metrics.py official repository unverified no licence file found · pointer only · d6da0ac5290c5a57 · report
weighted_mpjpe nhuang37/approx_equivariant_graph_nets/Human_Pose_Est/common/loss.py official repository unverified no licence file found · pointer only · 6dd3ba892d9349d8 · report

Tasks

Image InpaintingPose EstimationTranslation

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

ConvolutionFocus

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections