Methods › Graphs › Graph Models › GraphSAGE › Papers where code ran, page 1
GraphSAGE
Papers archive 2025-07-28
archive papers tagged: 132 · with a code link: 52 · where Syntology ran a sample: 15 (12 with a run with no instrument failure, 3 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (15 of 132 tagged: 12 with a run with no instrument failure, 3 where every run was a failure of Syntology's instrument)
Syntology We ran code from the paper's repository; we did not isolate this method inside it.
Page 1 of 1: papers 1 to 15 of the 15 tagged papers where Syntology ran at least one harvested sample (12 with a run with no instrument failure, 3 where every run was a failure of Syntology's instrument), newest first by the archive's date (ties by arXiv id). This is a filter on Syntology's record ordered by date only, not a ranking; a run is not a correctness claim. A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.
Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code, as “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the instrument figure counts failures of Syntology's instrument, not of the code. It is per sample and not a correctness claim. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code; “community repositories only” when every sample that ran came from a community repository, “official: no sample here; runs from other or unrecorded repositories” when some came from a repository the paper names or has in its text, or from none recorded); hover it for the repositories the samples that ran came from.
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Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis 31 Oct 2023 · 1 repository · arXiv:2311.00164Syntology official (archive's flag): 8 ran · 8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified (of 9 harvested samples) · 9 pointer-only (licence)
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Edge Directionality Improves Learning on Heterophilic Graphs 17 May 2023 · 1 repository · arXiv:2305.10498Syntology official (archive's flag): 4 ran · 4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 5 harvested samples) · 4 pointer-only (licence)
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A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs 11 Nov 2022 · 1 repository · arXiv:2211.06292Syntology official (archive's flag): 2 ran · 2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 3 harvested samples)
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MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization 30 Sep 2022 · 2 repositories · arXiv:2210.00102Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample) · 1 pointer-only (licence)
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BiFeat: Supercharge GNN Training via Graph Feature Quantization 29 Jul 2022 · 1 repository · arXiv:2207.14696Syntology official (archive's flag): 6 ran · 6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 6 harvested samples)
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SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning 11 Jul 2022 · 2 repositories · arXiv:2207.04606Syntology official (archive's flag): 6 ran · 6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 2 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 6 harvested samples)
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Decoupling the Depth and Scope of Graph Neural Networks 19 Jan 2022 · 1 repository · arXiv:2201.07858Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 2 harvested samples) · 2 pointer-only (licence)
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Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining 16 Oct 2021 · 2 repositories · arXiv:2110.08450Syntology official (archive's flag): 2 ran · 5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified (of 7 harvested samples)
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User Preference-aware Fake News Detection 25 Apr 2021 · 2 repositories · arXiv:2104.12259Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample)
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E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT 30 Mar 2021 · 3 repositories · arXiv:2103.16329Syntology community repositories only · 1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample) · 1 pointer-only (licence)
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GIST: Distributed Training for Large-Scale Graph Convolutional Networks 20 Feb 2021 · 1 repository · arXiv:2102.10424Syntology 2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified (of 2 harvested samples) · 2 pointer-only (licence)
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SCE: Scalable Network Embedding from Sparsest Cut 30 Jun 2020 · 1 repository · arXiv:2006.16499Syntology official (archive's flag): 6 ran · 6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified (of 7 harvested samples) · 2 pointer-only (licence)
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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification 25 Jul 2019 · 7 repositories · arXiv:1907.10903Syntology community repositories only · 4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 5 harvested samples)
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How Powerful are Graph Neural Networks? 1 Oct 2018 · 19 repositories · arXiv:1810.00826Syntology official (archive's flag): 2 ran · 6 ran (of which 2 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified (of 10 harvested samples) · 5 pointer-only (licence)
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Inductive Representation Learning on Large Graphs 7 Jun 2017 · 20 repositories · arXiv:1706.02216Syntology community repositories only · 3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified (of 5 harvested samples) · 5 pointer-only (licence)