{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:EI7RPFBQHX7MFFEWW6SIS5CZJT","short_pith_number":"pith:EI7RPFBQ","canonical_record":{"source":{"id":"2206.03695","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-08T06:29:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"51bfaa66edd20bf37450a826306670fda69b461eabc756afc32a50bf6f9e3f02","abstract_canon_sha256":"582e69b3e5caea83dc8a72f91021dbecef8c6d366f175d7f56e20dda595cedf1"},"schema_version":"1.0"},"canonical_sha256":"223f1794303dfec29496b7a48974594cdbcb171d0c7554f92b1dad09f5bd9882","source":{"kind":"arxiv","id":"2206.03695","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03695","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03695v3","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03695","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"pith_short_12","alias_value":"EI7RPFBQHX7M","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"pith_short_16","alias_value":"EI7RPFBQHX7MFFEW","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"pith_short_8","alias_value":"EI7RPFBQ","created_at":"2026-07-05T09:28:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:EI7RPFBQHX7MFFEWW6SIS5CZJT","target":"record","payload":{"canonical_record":{"source":{"id":"2206.03695","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-08T06:29:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"51bfaa66edd20bf37450a826306670fda69b461eabc756afc32a50bf6f9e3f02","abstract_canon_sha256":"582e69b3e5caea83dc8a72f91021dbecef8c6d366f175d7f56e20dda595cedf1"},"schema_version":"1.0"},"canonical_sha256":"223f1794303dfec29496b7a48974594cdbcb171d0c7554f92b1dad09f5bd9882","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:08.427702Z","signature_b64":"vczfSbL7qyo88AEVZqwTxnGsiM3oFSTrwxeBJBNeSh7c352LfDHcTskSrWNMxTNPHT1rFSroeSzsxb/5s8QVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"223f1794303dfec29496b7a48974594cdbcb171d0c7554f92b1dad09f5bd9882","last_reissued_at":"2026-07-05T09:28:08.427236Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:08.427236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.03695","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:28:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zYW0tYdnMr/y9ynHgf18SeUbWA27No6+cKb+1A37HE/TJM+3OWjw+wJh5MMEfU/TMlGCgKIUo9iCY056m2uXCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:34:35.988388Z"},"content_sha256":"334e0f4b04c44e41fe65f152455085202b987abfdbc7f95dc4e60719033a7da8","schema_version":"1.0","event_id":"sha256:334e0f4b04c44e41fe65f152455085202b987abfdbc7f95dc4e60719033a7da8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:EI7RPFBQHX7MFFEWW6SIS5CZJT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Metric Based Few-Shot Graph Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Donato Crisostomi, Emanuele Rodol\\`a, Luca Moschella, Riccardo Marin, Simone Antonelli, Valentino Maiorca","submitted_at":"2022-06-08T06:29:46Z","abstract_excerpt":"Many modern deep-learning techniques do not work without enormous datasets. At the same time, several fields demand methods working in scarcity of data. This problem is even more complex when the samples have varying structures, as in the case of graphs. Graph representation learning techniques have recently proven successful in a variety of domains. Nevertheless, the employed architectures perform miserably when faced with data scarcity. On the other hand, few-shot learning allows employing modern deep learning models in scarce data regimes without waiving their effectiveness. In this work, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03695","kind":"arxiv","version":3},"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/2206.03695/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":"2026-07-05T09:28:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xYYjhTuqs+glOMTQB9I5zH7JLSPTg0gRNEbbTFfpz7W1sukiUi0yExjagGKiXRQ7Z20daOHU8gbxXC+iqEkcBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:34:35.989201Z"},"content_sha256":"2d4789a7d8b06788ff8ecc2c4ea35560e1eba129deb3f2fec15be8238ab60aa3","schema_version":"1.0","event_id":"sha256:2d4789a7d8b06788ff8ecc2c4ea35560e1eba129deb3f2fec15be8238ab60aa3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EI7RPFBQHX7MFFEWW6SIS5CZJT/bundle.json","state_url":"https://pith.science/pith/EI7RPFBQHX7MFFEWW6SIS5CZJT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EI7RPFBQHX7MFFEWW6SIS5CZJT/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":"2026-08-18T13:34:35Z","links":{"resolver":"https://pith.science/pith/EI7RPFBQHX7MFFEWW6SIS5CZJT","bundle":"https://pith.science/pith/EI7RPFBQHX7MFFEWW6SIS5CZJT/bundle.json","state":"https://pith.science/pith/EI7RPFBQHX7MFFEWW6SIS5CZJT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EI7RPFBQHX7MFFEWW6SIS5CZJT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:EI7RPFBQHX7MFFEWW6SIS5CZJT","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":"582e69b3e5caea83dc8a72f91021dbecef8c6d366f175d7f56e20dda595cedf1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-08T06:29:46Z","title_canon_sha256":"51bfaa66edd20bf37450a826306670fda69b461eabc756afc32a50bf6f9e3f02"},"schema_version":"1.0","source":{"id":"2206.03695","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03695","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03695v3","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03695","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"pith_short_12","alias_value":"EI7RPFBQHX7M","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"pith_short_16","alias_value":"EI7RPFBQHX7MFFEW","created_at":"2026-07-05T09:28:08Z"},{"alias_kind":"pith_short_8","alias_value":"EI7RPFBQ","created_at":"2026-07-05T09:28:08Z"}],"graph_snapshots":[{"event_id":"sha256:2d4789a7d8b06788ff8ecc2c4ea35560e1eba129deb3f2fec15be8238ab60aa3","target":"graph","created_at":"2026-07-05T09:28:08Z","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/2206.03695/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many modern deep-learning techniques do not work without enormous datasets. At the same time, several fields demand methods working in scarcity of data. This problem is even more complex when the samples have varying structures, as in the case of graphs. Graph representation learning techniques have recently proven successful in a variety of domains. Nevertheless, the employed architectures perform miserably when faced with data scarcity. On the other hand, few-shot learning allows employing modern deep learning models in scarce data regimes without waiving their effectiveness. In this work, w","authors_text":"Donato Crisostomi, Emanuele Rodol\\`a, Luca Moschella, Riccardo Marin, Simone Antonelli, Valentino Maiorca","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-08T06:29:46Z","title":"Metric Based Few-Shot Graph Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03695","kind":"arxiv","version":3},"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":"sha256:334e0f4b04c44e41fe65f152455085202b987abfdbc7f95dc4e60719033a7da8","target":"record","created_at":"2026-07-05T09:28:08Z","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":"582e69b3e5caea83dc8a72f91021dbecef8c6d366f175d7f56e20dda595cedf1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-08T06:29:46Z","title_canon_sha256":"51bfaa66edd20bf37450a826306670fda69b461eabc756afc32a50bf6f9e3f02"},"schema_version":"1.0","source":{"id":"2206.03695","kind":"arxiv","version":3}},"canonical_sha256":"223f1794303dfec29496b7a48974594cdbcb171d0c7554f92b1dad09f5bd9882","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"223f1794303dfec29496b7a48974594cdbcb171d0c7554f92b1dad09f5bd9882","first_computed_at":"2026-07-05T09:28:08.427236Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:28:08.427236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vczfSbL7qyo88AEVZqwTxnGsiM3oFSTrwxeBJBNeSh7c352LfDHcTskSrWNMxTNPHT1rFSroeSzsxb/5s8QVDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:28:08.427702Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.03695","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:334e0f4b04c44e41fe65f152455085202b987abfdbc7f95dc4e60719033a7da8","sha256:2d4789a7d8b06788ff8ecc2c4ea35560e1eba129deb3f2fec15be8238ab60aa3"],"state_sha256":"0b31b0b1dc7a1e9e39fd86d4737ab3f033937d37a04efe88f2d4dbcfdff8e5b9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WXw+UxIlLbb4rSCKC1mv1TSR5HzNNtsnHJ9y2/t78Hb+CllzAnnqxZilPVwvBQ23DDU8ytENrh8g1WAEhww9Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T13:34:35.996764Z","bundle_sha256":"b099299244023a50a1018920257f1a2301a87576028810261d1e568d7ca920ac"}}