{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:SVWVBURNXSPI4NG6MGMO7S7CMS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0eeecf37715945bed5eb610376d3939a4b07b18fe7030e3c49c201f92c1e75a4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-18T08:03:12Z","title_canon_sha256":"954bbd85dced0f51c84ef3053640cb590f61ecd5c202d09028d47a4b28da91a1"},"schema_version":"1.0","source":{"id":"2206.09144","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.09144","created_at":"2026-07-05T05:28:45Z"},{"alias_kind":"arxiv_version","alias_value":"2206.09144v6","created_at":"2026-07-05T05:28:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.09144","created_at":"2026-07-05T05:28:45Z"},{"alias_kind":"pith_short_12","alias_value":"SVWVBURNXSPI","created_at":"2026-07-05T05:28:45Z"},{"alias_kind":"pith_short_16","alias_value":"SVWVBURNXSPI4NG6","created_at":"2026-07-05T05:28:45Z"},{"alias_kind":"pith_short_8","alias_value":"SVWVBURN","created_at":"2026-07-05T05:28:45Z"}],"graph_snapshots":[{"event_id":"sha256:f1ed9208d5a64d39cef1f5fdb13ba12cda9fe961a3e0bdf8c141b96156b2668b","target":"graph","created_at":"2026-07-05T05:28:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2206.09144/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the broad interest in developing and evaluating GNNs, they have been assessed with limited benchmark datasets. As a result, the existing evaluation of GNNs lacks fine-grained analysis from various characteristics of graphs. Motivated by this, we conduct extensive experiments with a synthetic graph generator that can generate graphs having controlled characteristics for fine-grained analysis. Our empirical studies clarify the strengths and weaknesses of GNNs from four major characteristics of real-wo","authors_text":"Koki Noda, Makoto Onizuka, Seiji Maekawa, Yuya Sasaki","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-18T08:03:12Z","title":"Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.09144","kind":"arxiv","version":6},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:585d6618f6ac3ae1f48a8f9afa7552c6a0706f2d65365f56688ad29a5326cf14","target":"record","created_at":"2026-07-05T05:28:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0eeecf37715945bed5eb610376d3939a4b07b18fe7030e3c49c201f92c1e75a4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-18T08:03:12Z","title_canon_sha256":"954bbd85dced0f51c84ef3053640cb590f61ecd5c202d09028d47a4b28da91a1"},"schema_version":"1.0","source":{"id":"2206.09144","kind":"arxiv","version":6}},"canonical_sha256":"956d50d22dbc9e8e34de6198efcbe26492b3b9e66df793480deef7ec54f43606","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"956d50d22dbc9e8e34de6198efcbe26492b3b9e66df793480deef7ec54f43606","first_computed_at":"2026-07-05T05:28:45.768363Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:28:45.768363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HmMhvKOS7gNwcnDwIW1qze5d2doFK6FVcyQreCIILkEgOpwaBvZdQQ/mqQtScDT+aphpYfxKBZevZecUIj7uDA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:28:45.768796Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.09144","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:585d6618f6ac3ae1f48a8f9afa7552c6a0706f2d65365f56688ad29a5326cf14","sha256:f1ed9208d5a64d39cef1f5fdb13ba12cda9fe961a3e0bdf8c141b96156b2668b"],"state_sha256":"544be6b9264ed11a85ad788e5cccfd3661c5fc4149df6a92cfee9a1b8be2c560"}