{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QBWNLXDYYEO4P7G6HXAA3V4ZTM","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":"cdd9b2dfce16a3d14eb9b6d8b2743fde5c55eff1f7cdbb5796337212aa2e019a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-14T04:21:52Z","title_canon_sha256":"e2e14e9c10069a877a9fade888e731903f7e3a55e0e1fac6c636d744c06a413e"},"schema_version":"1.0","source":{"id":"2310.09486","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.09486","created_at":"2026-07-05T08:02:46Z"},{"alias_kind":"arxiv_version","alias_value":"2310.09486v4","created_at":"2026-07-05T08:02:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.09486","created_at":"2026-07-05T08:02:46Z"},{"alias_kind":"pith_short_12","alias_value":"QBWNLXDYYEO4","created_at":"2026-07-05T08:02:46Z"},{"alias_kind":"pith_short_16","alias_value":"QBWNLXDYYEO4P7G6","created_at":"2026-07-05T08:02:46Z"},{"alias_kind":"pith_short_8","alias_value":"QBWNLXDY","created_at":"2026-07-05T08:02:46Z"}],"graph_snapshots":[{"event_id":"sha256:3d9b0fe9caff1a71b0c42abf6b570cbbf696a02f7f208d5dd1d663e192e404d8","target":"graph","created_at":"2026-07-05T08:02:46Z","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/2310.09486/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource environments. Graph distillation is an effort in that direction with the aim to construct a smaller synthetic training set from the original training data without significantly compromising model performance. While initial efforts are promising, this work is motivated by two key observations: (1) Existing graph distillation algorithms themselves rely on training with the full dataset, which undermines the very premise","authors_text":"Hariprasad Kodamana, Mridul Gupta, Sahil Manchanda, Sayan Ranu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-14T04:21:52Z","title":"Mirage: Model-Agnostic Graph Distillation for Graph Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.09486","kind":"arxiv","version":4},"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:bc7cee8a2aa7cbd4123868f7343c03b398b05539c140879005ba893ca0ec8ecc","target":"record","created_at":"2026-07-05T08:02:46Z","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":"cdd9b2dfce16a3d14eb9b6d8b2743fde5c55eff1f7cdbb5796337212aa2e019a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-14T04:21:52Z","title_canon_sha256":"e2e14e9c10069a877a9fade888e731903f7e3a55e0e1fac6c636d744c06a413e"},"schema_version":"1.0","source":{"id":"2310.09486","kind":"arxiv","version":4}},"canonical_sha256":"806cd5dc78c11dc7fcde3dc00dd7999b2544a620d189ea0ff306b45f26910e01","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"806cd5dc78c11dc7fcde3dc00dd7999b2544a620d189ea0ff306b45f26910e01","first_computed_at":"2026-07-05T08:02:46.860767Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:02:46.860767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IZiQSqvSqMilYRNdfxlkecOAPo2pgLT2J5C5KKqrYX5G+t8OOmO4dXXUZmOCOcVd5Nvs1kJnjfoHGrPBO5zwDw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:02:46.861338Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.09486","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc7cee8a2aa7cbd4123868f7343c03b398b05539c140879005ba893ca0ec8ecc","sha256:3d9b0fe9caff1a71b0c42abf6b570cbbf696a02f7f208d5dd1d663e192e404d8"],"state_sha256":"a178fde99c35a6f483af5c9a26dc448754c24c088fbfaf2015d49d92a63647f7"}