Papers › Gated Graph Sequence Neural Networks

Gated Graph Sequence Neural Networks

17 Nov 2015arXiv:1511.05493archive 2025-07-28

Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (Scarselli et al., 2009), which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences. The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence-based models (e.g., LSTMs) when the problem is graph-structured. We demonstrate the capabilities on some simple AI (bAbI) and graph algorithm learning tasks. We then show it achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.

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JamesChuanggg/ggnn.pytorch mentioned on GitHubpytorchMIT report
aszot/ggnn mentioned on GitHubpytorchMIT report
bdqnghi/bi-tbcnn mentioned on GitHubtf report
chingyaoc/ggnn.pytorch mentioned on GitHubpytorchMIT report
entslscheia/GGNN_Reasoning mentioned on GitHubpytorch report
fau-is/grm mentioned on GitHubtfMIT report
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class_from_prediction fau-is/grm/eval/baselines/testing.py community (archive-listed) unverified MIT (permissive) · 9826b26464a9c5c7 · report
data_tensor_from_sequences fau-is/grm/eval/baselines/preprocessing.py community (archive-listed) unverified MIT (permissive) · a49bd05a443ca196 · report
find_max_edge_id aszot/ggnn/dataset.py community (archive-listed) unverified MIT (permissive) · f4a2d232dcfa6b62 · report
find_max_node_id aszot/ggnn/dataset.py community (archive-listed) unverified MIT (permissive) · 77870b030fc21ba4 · report
label_tensor_from_sequences fau-is/grm/eval/baselines/preprocessing.py community (archive-listed) unverified MIT (permissive) · 26de65a3b1f8a63d · report
load_graphs_from_file aszot/ggnn/dataset.py community (archive-listed) unverified MIT (permissive) · 4fa3fedc0911aa4e · report
multi_class_prc_auc_score fau-is/grm/grm/util.py community (archive-listed) unverified MIT (permissive) · 57662fb4133ff587 · report
multi_class_roc_auc_score fau-is/grm/grm/util.py community (archive-listed) unverified MIT (permissive) · a6c201e613936dd7 · report
parse_dataset JamesChuanggg/ggnn.pytorch/babi_data/symbolic_preprocess.py community (archive-listed) unverified MIT (permissive) · 78c2c425c01a3a0b · report
parse_dataset JamesChuanggg/ggnn.pytorch/babi_data/extra_seq_tasks/preprocess.py community (archive-listed) unverified MIT (permissive) · 0e8807cf099640ba · report
save_confusion_matrix fau-is/grm/grm/util.py community (archive-listed) unverified MIT (permissive) · 27f86fb8017d14d0 · report
sequences_from_log_and_indices fau-is/grm/eval/baselines/preprocessing.py community (archive-listed) unverified MIT (permissive) · 8ab5fee5f3cbc14d · report

Tasks

Drug DiscoveryGraph ClassificationNode ClassificationSQL-to-Text

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Drug Discovery QM9 Gated Graph Sequence NN Error ratio 1.36 #10 of 11 Archive leaderboard report
Graph Classification IPC-grounded GG-NN Accuracy 77.9% #1 of 2 Archive leaderboard report
Graph Classification IPC-lifted GG-NN Accuracy 81.4% #2 of 2 Archive leaderboard report
Node Classification CiteSeer (1%) GGNN Accuracy 56.0% #11 of 14 Archive leaderboard report
Node Classification CiteSeer with Public Split: fixed 20 nodes per class GGNN Accuracy 64.6% #38 of 40 Archive leaderboard report
Node Classification Cora (0.5%) GGNN Accuracy 48.2% #12 of 15 Archive leaderboard report
Node Classification Cora (1%) GGNN Accuracy 60.5% #11 of 15 Archive leaderboard report
Node Classification Cora (3%) GGNN Accuracy 73.1% #11 of 15 Archive leaderboard report
Node Classification Cora with Public Split: fixed 20 nodes per class GGNN Accuracy 77.6% #34 of 36 Archive leaderboard report
Node Classification PubMed (0.03%) GGNN Accuracy 55.8% #11 of 14 Archive leaderboard report
Node Classification PubMed (0.05%) GGNN Accuracy 63.3% #10 of 14 Archive leaderboard report
Node Classification PubMed (0.1%) GGNN Accuracy 70.4% #10 of 14 Archive leaderboard report
Node Classification PubMed with Public Split: fixed 20 nodes per class GGNN Accuracy 75.8% #32 of 37 Archive leaderboard report
SQL-to-Text WikiSQL GGS-NN BLEU-4 35.53 #2 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

GGS-NNs

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