{"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/a-new-graph-node-classification-benchmark","title":"A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs","arxiv_id":"2211.06292","date":"2022-11-11","proceeding":null,"authors":["Claudia Vanea","Jonathan Campbell","Omri Dodi","Liis Salumäe","Karen Meir","Drorith Hochner-Celnikier","Hagit Hochner","Triin Laisk","Linda M. Ernst","Cecilia M. Lindgren","Christoffer Nellåker"],"abstract":"We introduce a new benchmark dataset, Placenta, for node classification in an underexplored domain: predicting microanatomical tissue structures from cell graphs in placenta histology whole slide images. This problem is uniquely challenging for graph learning for a few reasons. Cell graphs are large (>1 million nodes per image), node features are varied (64-dimensions of 11 types of cells), class labels are imbalanced (9 classes ranging from 0.21% of the data to 40.0%), and cellular communities cluster into heterogeneously distributed tissues of widely varying sizes (from 11 nodes to 44,671 nodes for a single structure). Here, we release a dataset consisting of two cell graphs from two placenta histology images totalling 2,395,747 nodes, 799,745 of which have ground truth labels. We present inductive benchmark results for 7 scalable models and show how the unique qualities of cell graphs can help drive the development of novel graph neural network architectures.","url_abs":"https://arxiv.org/abs/2211.06292v1","url_pdf":"https://arxiv.org/pdf/2211.06292v1.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":"a-new-graph-node-classification-benchmark","repo_url":"https://github.com/nellaker-group/placenta","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[{"method_slug":"cluster-gcn","method_name":"Cluster-GCN"},{"method_slug":"gat","method_name":"GAT"},{"method_slug":"gatv2","method_name":"GATv2"},{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"},{"method_slug":"graphsage","method_name":"GraphSAGE"},{"method_slug":"graphsaint","method_name":"GraphSAINT"}],"datasets_introduced":[{"slug":"placenta","name":"Placenta","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-placenta","task":"Node Classification","dataset":"Placenta","model":"GraphSAGE","rank_in_archive_order":1,"of":5,"metrics":{"Accuracy (%)":"64.88±0.43"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-placenta","task":"Node Classification","dataset":"Placenta","model":"SIGN","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy (%)":"64.77±0.43"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-placenta","task":"Node Classification","dataset":"Placenta","model":"ClusterGCN","rank_in_archive_order":3,"of":5,"metrics":{"Accuracy (%)":"64.24±1.21"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-placenta","task":"Node Classification","dataset":"Placenta","model":"GraphSAINT","rank_in_archive_order":4,"of":5,"metrics":{"Accuracy (%)":"63.94±0.23"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-placenta","task":"Node Classification","dataset":"Placenta","model":"ShaDow","rank_in_archive_order":5,"of":5,"metrics":{"Accuracy (%)":"63.04±0.77"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.06292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.06292"}},"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":"deterministic:regex_extraction","url":"https://github.com/Nellaker-group/placenta","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nellaker-group/placenta","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"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":"3b76433c473cb3c9","entry":"get_nodes_within_tiles","repo":"Nellaker-group/placenta","repo_kind":"official","path":"placenta/dataset.py","file_url":"https://github.com/Nellaker-group/placenta/blob/HEAD/placenta/dataset.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":"3b76433c473cb3c9"}},{"code_sha256_prefix":"0cf316f93d5ce978","entry":"get_tissue_confusion_matrix","repo":"Nellaker-group/placenta","repo_kind":"official","path":"placenta/evaluation_plots.py","file_url":"https://github.com/Nellaker-group/placenta/blob/HEAD/placenta/evaluation_plots.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":"0cf316f93d5ce978"}},{"code_sha256_prefix":"126c3b301e4eb0d5","entry":"setup_run","repo":"Nellaker-group/placenta","repo_kind":"official","path":"placenta/utils.py","file_url":"https://github.com/Nellaker-group/placenta/blob/HEAD/placenta/utils.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":"126c3b301e4eb0d5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}