{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:3EEB46R6SGOTAUXY2YKW36KAK4","short_pith_number":"pith:3EEB46R6","canonical_record":{"source":{"id":"2205.10666","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-21T20:04:46Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"55a1235f0b560038f6cffdcb103040e9f304ca9523c90ea3b7b4921bd9b600fb","abstract_canon_sha256":"8cb7552e3a02c5a47c5ef7bd5cb2c56d16f11563e9eafbdc3f9d513e57130021"},"schema_version":"1.0"},"canonical_sha256":"d9081e7a3e919d3052f8d6156df940572d5544873b842031495761a4e9ab61ff","source":{"kind":"arxiv","id":"2205.10666","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.10666","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"arxiv_version","alias_value":"2205.10666v1","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.10666","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"pith_short_12","alias_value":"3EEB46R6SGOT","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"pith_short_16","alias_value":"3EEB46R6SGOTAUXY","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"pith_short_8","alias_value":"3EEB46R6","created_at":"2026-07-05T04:25:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:3EEB46R6SGOTAUXY2YKW36KAK4","target":"record","payload":{"canonical_record":{"source":{"id":"2205.10666","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-21T20:04:46Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"55a1235f0b560038f6cffdcb103040e9f304ca9523c90ea3b7b4921bd9b600fb","abstract_canon_sha256":"8cb7552e3a02c5a47c5ef7bd5cb2c56d16f11563e9eafbdc3f9d513e57130021"},"schema_version":"1.0"},"canonical_sha256":"d9081e7a3e919d3052f8d6156df940572d5544873b842031495761a4e9ab61ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:25:22.521674Z","signature_b64":"pkMFgGoos7oqTH6vwdCt8QqZ9Ng8Rpw8eR+Kw2VrpJDBw0jNtsk0TSC3OPThCryYPULygzTaof5Fh576cvEVAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9081e7a3e919d3052f8d6156df940572d5544873b842031495761a4e9ab61ff","last_reissued_at":"2026-07-05T04:25:22.521293Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:25:22.521293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.10666","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":"2026-07-05T04:25:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rB8nz+UaY/MhdfjS6M8znekf1sNCSv4AIuBL1bPVWR9tFMYUe8oQzMI8QQ0gzlA8kcJezvekv/gLLMCox0zADA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T16:02:48.297679Z"},"content_sha256":"7b0be2361df75b80e1dda382c0945f38cae43cfd342553c632d15097477c01bc","schema_version":"1.0","event_id":"sha256:7b0be2361df75b80e1dda382c0945f38cae43cfd342553c632d15097477c01bc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:3EEB46R6SGOTAUXY2YKW36KAK4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.LG","authors_text":"Andrew Zhai, Charles Rosenberg, Eric Kim, Jure Leskovec, Nikil Pancha, Saket Gurukar, Samson Hu, Srinivasan Parthasarathy","submitted_at":"2022-05-21T20:04:46Z","abstract_excerpt":"Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several tasks such as recommendation and search. At Pinterest, we have developed and deployed PinSage, a data-efficient GCN that learns pin embeddings from the Pin-Board graph. The Pin-Board graph contains pin and board entities and the graph captures the pin belongs to a board interaction. However, there exist several entities at Pinterest such as users, idea pins, creators, and there exist heterogeneous interactions among "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.10666","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/2205.10666/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":"2026-07-05T04:25:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"llq+CjTy4iFqvYy77195abC5+ykJ7H+R//FR2K2xDj0nRKPbUf926sSJQZzGg13+XNPRGz2YHDdMrQdNk70DCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T16:02:48.298144Z"},"content_sha256":"48f211c5d8acf258b11bccaeb47b91673f3997a44561e0cc904913a6edc3a6f9","schema_version":"1.0","event_id":"sha256:48f211c5d8acf258b11bccaeb47b91673f3997a44561e0cc904913a6edc3a6f9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3EEB46R6SGOTAUXY2YKW36KAK4/bundle.json","state_url":"https://pith.science/pith/3EEB46R6SGOTAUXY2YKW36KAK4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3EEB46R6SGOTAUXY2YKW36KAK4/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":"2026-08-20T16:02:48Z","links":{"resolver":"https://pith.science/pith/3EEB46R6SGOTAUXY2YKW36KAK4","bundle":"https://pith.science/pith/3EEB46R6SGOTAUXY2YKW36KAK4/bundle.json","state":"https://pith.science/pith/3EEB46R6SGOTAUXY2YKW36KAK4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3EEB46R6SGOTAUXY2YKW36KAK4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3EEB46R6SGOTAUXY2YKW36KAK4","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":"8cb7552e3a02c5a47c5ef7bd5cb2c56d16f11563e9eafbdc3f9d513e57130021","cross_cats_sorted":["cs.SI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-21T20:04:46Z","title_canon_sha256":"55a1235f0b560038f6cffdcb103040e9f304ca9523c90ea3b7b4921bd9b600fb"},"schema_version":"1.0","source":{"id":"2205.10666","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.10666","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"arxiv_version","alias_value":"2205.10666v1","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.10666","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"pith_short_12","alias_value":"3EEB46R6SGOT","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"pith_short_16","alias_value":"3EEB46R6SGOTAUXY","created_at":"2026-07-05T04:25:22Z"},{"alias_kind":"pith_short_8","alias_value":"3EEB46R6","created_at":"2026-07-05T04:25:22Z"}],"graph_snapshots":[{"event_id":"sha256:48f211c5d8acf258b11bccaeb47b91673f3997a44561e0cc904913a6edc3a6f9","target":"graph","created_at":"2026-07-05T04:25:22Z","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/2205.10666/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several tasks such as recommendation and search. At Pinterest, we have developed and deployed PinSage, a data-efficient GCN that learns pin embeddings from the Pin-Board graph. The Pin-Board graph contains pin and board entities and the graph captures the pin belongs to a board interaction. However, there exist several entities at Pinterest such as users, idea pins, creators, and there exist heterogeneous interactions among ","authors_text":"Andrew Zhai, Charles Rosenberg, Eric Kim, Jure Leskovec, Nikil Pancha, Saket Gurukar, Samson Hu, Srinivasan Parthasarathy","cross_cats":["cs.SI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-21T20:04:46Z","title":"MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.10666","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":"sha256:7b0be2361df75b80e1dda382c0945f38cae43cfd342553c632d15097477c01bc","target":"record","created_at":"2026-07-05T04:25:22Z","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":"8cb7552e3a02c5a47c5ef7bd5cb2c56d16f11563e9eafbdc3f9d513e57130021","cross_cats_sorted":["cs.SI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-21T20:04:46Z","title_canon_sha256":"55a1235f0b560038f6cffdcb103040e9f304ca9523c90ea3b7b4921bd9b600fb"},"schema_version":"1.0","source":{"id":"2205.10666","kind":"arxiv","version":1}},"canonical_sha256":"d9081e7a3e919d3052f8d6156df940572d5544873b842031495761a4e9ab61ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d9081e7a3e919d3052f8d6156df940572d5544873b842031495761a4e9ab61ff","first_computed_at":"2026-07-05T04:25:22.521293Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:25:22.521293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pkMFgGoos7oqTH6vwdCt8QqZ9Ng8Rpw8eR+Kw2VrpJDBw0jNtsk0TSC3OPThCryYPULygzTaof5Fh576cvEVAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:25:22.521674Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.10666","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b0be2361df75b80e1dda382c0945f38cae43cfd342553c632d15097477c01bc","sha256:48f211c5d8acf258b11bccaeb47b91673f3997a44561e0cc904913a6edc3a6f9"],"state_sha256":"924c37d883834df304506d2dbb505d65f60b99b506cc55c6045fd8d8b0f10d9e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ATivzOkcyYGxAz2ekruQJy9Lq4yHhLP6V80Hex3QKLiJKKSPpYJRyvR5JMIqom197yGYJZ5NSV+ZwsHxnMFbBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T16:02:48.300737Z","bundle_sha256":"b73daacd6ab613422e49585ca23160f3d832d17c51c47c709248fe866b2d79a9"}}