Papers › Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning

26 May 2024arXiv:2405.16435archive 2025-07-28

Yuankai Luo, Hongkang Li, Qijiong Liu, Lei Shi, Xiao-Ming Wu

We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identifiers (node IDs), to tackle inference challenges on large-scale graphs. By employing vector quantization, we compress continuous node embeddings from multiple layers of a Graph Neural Network (GNN) into discrete codes, applicable under both self-supervised and supervised learning paradigms. These node IDs capture high-level abstractions of graph data and offer interpretability that traditional GNN embeddings lack. Extensive experiments on 34 datasets, encompassing node classification, graph classification, link prediction, and attributed graph clustering tasks, demonstrate that the generated node IDs significantly enhance speed and memory efficiency while achieving competitive performance compared to current state-of-the-art methods.

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LUOyk1999/NodeID officialmentioned in papermentioned on GitHubpytorchMIT report

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compute_loss LUOyk1999/NodeID/SL/Graph_Classification/ID_MLP_f.py official repository ran MIT (permissive) · 135fdcc9a2764c3c · report
default LUOyk1999/NodeID/SL/Link_Prediction/vq.py official repository ran · violated contract fingerprinted MIT (permissive) · 60fff7c3c400d7ff · report
eval_spearmanr LUOyk1999/NodeID/SL/Graph_Classification/ID_MLP_s.py official repository ran MIT (permissive) · cca15e2cb74d7749 · report
exists LUOyk1999/NodeID/SL/Link_Prediction/vq.py official repository ran · violated contract MIT (permissive) · aa5486a3650902d8 · report
get_final_pretrained_ckpt LUOyk1999/NodeID/SL/Graph_Classification/graphgps/finetuning.py official repository ran MIT (permissive) · 1bb331bf70a78136 · report
index2mask LUOyk1999/NodeID/SL/Graph_Classification/ID_MLP_f.py official repository ran MIT (permissive) · 10e5b33e372f7d10 · report
l1_losses LUOyk1999/NodeID/SL/Graph_Classification/ID_MLP_s.py official repository ran fingerprinted MIT (permissive) · 834eb82b9c7d878b · report
l2norm LUOyk1999/NodeID/SL/Link_Prediction/vq.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · eca6cdf05972a95e · report
load_out_t LUOyk1999/NodeID/SL/Link_Prediction/ID_MLP.py official repository ran MIT (permissive) · 372636b338d97593 · report
randomsplit LUOyk1999/NodeID/SL/Link_Prediction/ogbdataset.py official repository ran MIT (permissive) · 7889dfc69061e8f0 · report
init_model_from_pretrained LUOyk1999/NodeID/SL/Graph_Classification/graphgps/finetuning.py official repository unverified MIT (permissive) · 3db4474abe13fdee · report
load_pretrained_model_cfg LUOyk1999/NodeID/SL/Graph_Classification/graphgps/finetuning.py official repository unverified MIT (permissive) · d796cdea5889c847 · report
regression LUOyk1999/NodeID/SL/Graph_Classification/ID_MLP_s.py official repository unverified MIT (permissive) · 81102467886bdbff · report

Tasks

Computational EfficiencyGraph ClassificationGraph ClusteringGraph LearningGraph Neural NetworkLink PredictionNode ClassificationQuantization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Node Classification questions NID AUCROC 97.03 ± 0.02 #1 of 3 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

Graph Neural NetworkSPEED

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