{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/taxonomy-of-benchmarks-in-graph","title":"Taxonomy of Benchmarks in Graph Representation Learning","arxiv_id":"2206.07729","date":"2022-06-15","proceeding":null,"authors":["Renming Liu","Semih Cantürk","Frederik Wenkel","Sarah McGuire","Xinyi Wang","Anna Little","Leslie O'Bray","Michael Perlmutter","Bastian Rieck","Matthew Hirn","Guy Wolf","Ladislav Rampášek"],"abstract":"Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collection of graph representation learning benchmarks, it is currently not well understood what aspects of a given model are probed by them. For example, to what extent do they test the ability of a model to leverage graph structure vs. node features? Here, we develop a principled approach to taxonomize benchmarking datasets according to a $\\textit{sensitivity profile}$ that is based on how much GNN performance changes due to a collection of graph perturbations. Our data-driven analysis provides a deeper understanding of which benchmarking data characteristics are leveraged by GNNs. Consequently, our taxonomy can aid in selection and development of adequate graph benchmarks, and better informed evaluation of future GNN methods. Finally, our approach and implementation in $\\texttt{GTaxoGym}$ package are extendable to multiple graph prediction task types and future datasets.","url_abs":"https://arxiv.org/abs/2206.07729v4","url_pdf":"https://arxiv.org/pdf/2206.07729v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"taxonomy-of-benchmarks-in-graph","repo_url":"https://github.com/g-taxonomy-workgroup/gtaxogym","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.07729","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07729"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/g-taxonomy-workgroup/gtaxogym","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"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"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"cf8c5ee5d2981935","entry":"loss_example","repo":"g-taxonomy-workgroup/gtaxogym","repo_kind":"official","path":"gtaxogym/loss/example.py","file_url":"https://github.com/g-taxonomy-workgroup/gtaxogym/blob/HEAD/gtaxogym/loss/example.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cf8c5ee5d2981935"}},{"code_sha256_prefix":"5ab8146d0576b61e","entry":"multilabel_cross_entropy","repo":"g-taxonomy-workgroup/gtaxogym","repo_kind":"official","path":"gtaxogym/loss/multilabel_classification_loss.py","file_url":"https://github.com/g-taxonomy-workgroup/gtaxogym/blob/HEAD/gtaxogym/loss/multilabel_classification_loss.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5ab8146d0576b61e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}