{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:72E7LHE6M3EZ2QO62M52QWGH53","short_pith_number":"pith:72E7LHE6","canonical_record":{"source":{"id":"2111.06283","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T15:48:59Z","cross_cats_sorted":[],"title_canon_sha256":"04063fea1629dcc2559de924d7b50ddc8ffa5954220f97b262dbe37a6f95a094","abstract_canon_sha256":"b4dc4f2d4fd79744f34b993de52282c4652afa7032943aaef807247a80e23a21"},"schema_version":"1.0"},"canonical_sha256":"fe89f59c9e66c99d41ded33ba858c7eee96584ff0bd0ae65dbb016680f271df9","source":{"kind":"arxiv","id":"2111.06283","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.06283","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"arxiv_version","alias_value":"2111.06283v1","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.06283","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"pith_short_12","alias_value":"72E7LHE6M3EZ","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"72E7LHE6M3EZ2QO6","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"72E7LHE6","created_at":"2026-07-05T03:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:72E7LHE6M3EZ2QO62M52QWGH53","target":"record","payload":{"canonical_record":{"source":{"id":"2111.06283","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T15:48:59Z","cross_cats_sorted":[],"title_canon_sha256":"04063fea1629dcc2559de924d7b50ddc8ffa5954220f97b262dbe37a6f95a094","abstract_canon_sha256":"b4dc4f2d4fd79744f34b993de52282c4652afa7032943aaef807247a80e23a21"},"schema_version":"1.0"},"canonical_sha256":"fe89f59c9e66c99d41ded33ba858c7eee96584ff0bd0ae65dbb016680f271df9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:31:00.010876Z","signature_b64":"Zo5sOXyY2wto8zxx8DZ1Z5mjPqEuPAahtRrqttepp5+9oKIrQBhgHkNW/8lNHiDAh6VQWLvBIGtsx55ijRvZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe89f59c9e66c99d41ded33ba858c7eee96584ff0bd0ae65dbb016680f271df9","last_reissued_at":"2026-07-05T03:31:00.010431Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:31:00.010431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.06283","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:31:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8G8ERJ12nr4LPPM+XK+Wg2Dlb1wlQRQddJkqCvrhr77E2qOZ8Qhbkfc0/NrzzZKomYiRinQXleEh07cN9rSQBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T00:44:11.131989Z"},"content_sha256":"cd144772616329208a184e3b1e8edcf1437ab0a60f73210209baddea097c7f89","schema_version":"1.0","event_id":"sha256:cd144772616329208a184e3b1e8edcf1437ab0a60f73210209baddea097c7f89"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:72E7LHE6M3EZ2QO62M52QWGH53","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Karolis Martinkus, Lukas Faber, P\\'al Andr\\'as Papp, Roger Wattenhofer","submitted_at":"2021-11-11T15:48:59Z","abstract_excerpt":"This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks. In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs. Then, we combine the results of these runs to obtain the final result. We prove that DropGNNs can distinguish various graph neighborhoods that cannot be separated by message passing GNNs. We derive theoretical bounds for the number of runs required to ensure a reliable distribution of dropouts, and we prove s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.06283","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.06283/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:31:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b7W6EuttfSRWHFjp8ZBAYnvkToHghHsEQG5bdoeCIM0mWpA2U+VQZqZ571Y0nLi/+kqskN9GyOY5qbbwtKQ5AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T00:44:11.132787Z"},"content_sha256":"08fceed0dbd635abb7502705eb5101b93695be3e08e242518f7ad7b060daffed","schema_version":"1.0","event_id":"sha256:08fceed0dbd635abb7502705eb5101b93695be3e08e242518f7ad7b060daffed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/72E7LHE6M3EZ2QO62M52QWGH53/bundle.json","state_url":"https://pith.science/pith/72E7LHE6M3EZ2QO62M52QWGH53/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/72E7LHE6M3EZ2QO62M52QWGH53/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T00:44:11Z","links":{"resolver":"https://pith.science/pith/72E7LHE6M3EZ2QO62M52QWGH53","bundle":"https://pith.science/pith/72E7LHE6M3EZ2QO62M52QWGH53/bundle.json","state":"https://pith.science/pith/72E7LHE6M3EZ2QO62M52QWGH53/state.json","well_known_bundle":"https://pith.science/.well-known/pith/72E7LHE6M3EZ2QO62M52QWGH53/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:72E7LHE6M3EZ2QO62M52QWGH53","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":"b4dc4f2d4fd79744f34b993de52282c4652afa7032943aaef807247a80e23a21","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T15:48:59Z","title_canon_sha256":"04063fea1629dcc2559de924d7b50ddc8ffa5954220f97b262dbe37a6f95a094"},"schema_version":"1.0","source":{"id":"2111.06283","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.06283","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"arxiv_version","alias_value":"2111.06283v1","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.06283","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"pith_short_12","alias_value":"72E7LHE6M3EZ","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"72E7LHE6M3EZ2QO6","created_at":"2026-07-05T03:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"72E7LHE6","created_at":"2026-07-05T03:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:08fceed0dbd635abb7502705eb5101b93695be3e08e242518f7ad7b060daffed","target":"graph","created_at":"2026-07-05T03:31:00Z","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/2111.06283/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks. In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs. Then, we combine the results of these runs to obtain the final result. We prove that DropGNNs can distinguish various graph neighborhoods that cannot be separated by message passing GNNs. We derive theoretical bounds for the number of runs required to ensure a reliable distribution of dropouts, and we prove s","authors_text":"Karolis Martinkus, Lukas Faber, P\\'al Andr\\'as Papp, Roger Wattenhofer","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T15:48:59Z","title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.06283","kind":"arxiv","version":1},"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:cd144772616329208a184e3b1e8edcf1437ab0a60f73210209baddea097c7f89","target":"record","created_at":"2026-07-05T03:31:00Z","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":"b4dc4f2d4fd79744f34b993de52282c4652afa7032943aaef807247a80e23a21","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T15:48:59Z","title_canon_sha256":"04063fea1629dcc2559de924d7b50ddc8ffa5954220f97b262dbe37a6f95a094"},"schema_version":"1.0","source":{"id":"2111.06283","kind":"arxiv","version":1}},"canonical_sha256":"fe89f59c9e66c99d41ded33ba858c7eee96584ff0bd0ae65dbb016680f271df9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe89f59c9e66c99d41ded33ba858c7eee96584ff0bd0ae65dbb016680f271df9","first_computed_at":"2026-07-05T03:31:00.010431Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:31:00.010431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zo5sOXyY2wto8zxx8DZ1Z5mjPqEuPAahtRrqttepp5+9oKIrQBhgHkNW/8lNHiDAh6VQWLvBIGtsx55ijRvZCw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:31:00.010876Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.06283","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cd144772616329208a184e3b1e8edcf1437ab0a60f73210209baddea097c7f89","sha256:08fceed0dbd635abb7502705eb5101b93695be3e08e242518f7ad7b060daffed"],"state_sha256":"b259a86d5fea62d17f2df1e0e4d0f3b75be4841d18cce1b8062b00520df2044a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O4xk9O7jdUBcnc+cKBm1Ps+5BBKNAASkuVoIHKgBUxPxw2U5wGydsdDW1E8RT8OY5r0TAViolzNEd+GwIs+OCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T00:44:11.138802Z","bundle_sha256":"fc08b211a6589fdf701895940bd9cd07f1ead21b6f2fce7b0aafecfe8a6537c9"}}