Methods › Graphs › Graph Models › GIN › Papers where code ran, page 1
Graph Isomorphism Network
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Papers archive 2025-07-28
archive papers tagged: 43 · with a code link: 25 · where Syntology ran a sample: 5 (5 with a run with no instrument failure, 0 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (5 of 43 tagged: 5 with a run with no instrument failure, 0 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 5 of the 5 tagged papers where Syntology ran at least one harvested sample (5 with a run with no instrument failure, 0 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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Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence 13 Feb 2025 · 1 repository · arXiv:2502.09263Syntology official (archive's flag): 7 ran · 7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified (of 12 harvested samples) · 3 pointer-only (licence)
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A Simple and Yet Fairly Effective Defense for Graph Neural Networks 21 Feb 2024 · 1 repository · arXiv:2402.13987Syntology 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) · 2 unverified (of 3 harvested samples) · 1 pointer-only (licence)
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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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On the Bottleneck of Graph Neural Networks and its Practical Implications 9 Jun 2020 · 3 repositories · arXiv:2006.05205Syntology 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)
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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)