{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:MXPL6CMYTLUHHRGRNYMWTBEG27","short_pith_number":"pith:MXPL6CMY","canonical_record":{"source":{"id":"2210.07769","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-09-25T12:53:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"347bc4e0550b57846ccba22084b10784b2785dceaf9ea6842b430648a7c2bece","abstract_canon_sha256":"a4e62e759d0efb8fc7179b27f284c1ec6d9c79c2f23784d04098cac790bef7e4"},"schema_version":"1.0"},"canonical_sha256":"65debf09989ae873c4d16e19698486d7e5aa4dd57211cc2f62342b70386f8ecc","source":{"kind":"arxiv","id":"2210.07769","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.07769","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"arxiv_version","alias_value":"2210.07769v1","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.07769","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"pith_short_12","alias_value":"MXPL6CMYTLUH","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"pith_short_16","alias_value":"MXPL6CMYTLUHHRGR","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"pith_short_8","alias_value":"MXPL6CMY","created_at":"2026-07-05T05:06:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:MXPL6CMYTLUHHRGRNYMWTBEG27","target":"record","payload":{"canonical_record":{"source":{"id":"2210.07769","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-09-25T12:53:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"347bc4e0550b57846ccba22084b10784b2785dceaf9ea6842b430648a7c2bece","abstract_canon_sha256":"a4e62e759d0efb8fc7179b27f284c1ec6d9c79c2f23784d04098cac790bef7e4"},"schema_version":"1.0"},"canonical_sha256":"65debf09989ae873c4d16e19698486d7e5aa4dd57211cc2f62342b70386f8ecc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:06:40.894649Z","signature_b64":"TFlYKuU6nuWyr71bj/XyAGNFr0KXb3Sbyicd2SV3ZemM2qrSTAOrnM5XVBLPHZqpIqky5d2tYOYkDXBhdOg0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65debf09989ae873c4d16e19698486d7e5aa4dd57211cc2f62342b70386f8ecc","last_reissued_at":"2026-07-05T05:06:40.894184Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:06:40.894184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.07769","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":"2026-07-05T05:06:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xrWBtoYvRqEuuQi0U5S0lDmANit1BzLc4zQy4xVdn3rgfX8AJnlG2hEoTHS9Z927EKuIWmDJ7RAEnj4Rh9/2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:18:36.800407Z"},"content_sha256":"5935589739bb98ddbd434cc2e6f46e8bf168d3c553bd8a250dbb2c2a03f018d8","schema_version":"1.0","event_id":"sha256:5935589739bb98ddbd434cc2e6f46e8bf168d3c553bd8a250dbb2c2a03f018d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:MXPL6CMYTLUHHRGRNYMWTBEG27","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Flattened Graph Convolutional Networks For Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Feiran Huang, Hao Chen, Yuanchen Bei, Yue Xu, Zengde Deng","submitted_at":"2022-09-25T12:53:50Z","abstract_excerpt":"Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform recursive aggregations among all related nodes, which can arise severe computational burden to hinder their application to large-scale recommendation tasks. To this end, this paper proposes the flattened GCN~(FlatGCN) model, which is able to achieve superior performance with remarkably less complexity compared with existing models. Our main contribution is three-fold. First, we propose a simplified but powerful GCN arch"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.07769","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/2210.07769/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":"2026-07-05T05:06:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ve9jyn4Cr2VY4yDXn1E2baKYaLi5WIfXVvslrHBdtQ1rCpvXx3X9PhhsyngM1pxAeNGJNV6rmXmCIDhgulqWBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:18:36.801028Z"},"content_sha256":"077099295994ee72d5d29059580fcf4620bc62ad8dd191bf18e0d28bf38a4598","schema_version":"1.0","event_id":"sha256:077099295994ee72d5d29059580fcf4620bc62ad8dd191bf18e0d28bf38a4598"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MXPL6CMYTLUHHRGRNYMWTBEG27/bundle.json","state_url":"https://pith.science/pith/MXPL6CMYTLUHHRGRNYMWTBEG27/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MXPL6CMYTLUHHRGRNYMWTBEG27/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":"2026-08-05T03:18:36Z","links":{"resolver":"https://pith.science/pith/MXPL6CMYTLUHHRGRNYMWTBEG27","bundle":"https://pith.science/pith/MXPL6CMYTLUHHRGRNYMWTBEG27/bundle.json","state":"https://pith.science/pith/MXPL6CMYTLUHHRGRNYMWTBEG27/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MXPL6CMYTLUHHRGRNYMWTBEG27/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:MXPL6CMYTLUHHRGRNYMWTBEG27","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":"a4e62e759d0efb8fc7179b27f284c1ec6d9c79c2f23784d04098cac790bef7e4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-09-25T12:53:50Z","title_canon_sha256":"347bc4e0550b57846ccba22084b10784b2785dceaf9ea6842b430648a7c2bece"},"schema_version":"1.0","source":{"id":"2210.07769","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.07769","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"arxiv_version","alias_value":"2210.07769v1","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.07769","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"pith_short_12","alias_value":"MXPL6CMYTLUH","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"pith_short_16","alias_value":"MXPL6CMYTLUHHRGR","created_at":"2026-07-05T05:06:40Z"},{"alias_kind":"pith_short_8","alias_value":"MXPL6CMY","created_at":"2026-07-05T05:06:40Z"}],"graph_snapshots":[{"event_id":"sha256:077099295994ee72d5d29059580fcf4620bc62ad8dd191bf18e0d28bf38a4598","target":"graph","created_at":"2026-07-05T05:06:40Z","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/2210.07769/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform recursive aggregations among all related nodes, which can arise severe computational burden to hinder their application to large-scale recommendation tasks. To this end, this paper proposes the flattened GCN~(FlatGCN) model, which is able to achieve superior performance with remarkably less complexity compared with existing models. Our main contribution is three-fold. First, we propose a simplified but powerful GCN arch","authors_text":"Feiran Huang, Hao Chen, Yuanchen Bei, Yue Xu, Zengde Deng","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-09-25T12:53:50Z","title":"Flattened Graph Convolutional Networks For Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.07769","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":"sha256:5935589739bb98ddbd434cc2e6f46e8bf168d3c553bd8a250dbb2c2a03f018d8","target":"record","created_at":"2026-07-05T05:06:40Z","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":"a4e62e759d0efb8fc7179b27f284c1ec6d9c79c2f23784d04098cac790bef7e4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-09-25T12:53:50Z","title_canon_sha256":"347bc4e0550b57846ccba22084b10784b2785dceaf9ea6842b430648a7c2bece"},"schema_version":"1.0","source":{"id":"2210.07769","kind":"arxiv","version":1}},"canonical_sha256":"65debf09989ae873c4d16e19698486d7e5aa4dd57211cc2f62342b70386f8ecc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"65debf09989ae873c4d16e19698486d7e5aa4dd57211cc2f62342b70386f8ecc","first_computed_at":"2026-07-05T05:06:40.894184Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:06:40.894184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TFlYKuU6nuWyr71bj/XyAGNFr0KXb3Sbyicd2SV3ZemM2qrSTAOrnM5XVBLPHZqpIqky5d2tYOYkDXBhdOg0DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:06:40.894649Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.07769","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5935589739bb98ddbd434cc2e6f46e8bf168d3c553bd8a250dbb2c2a03f018d8","sha256:077099295994ee72d5d29059580fcf4620bc62ad8dd191bf18e0d28bf38a4598"],"state_sha256":"a454400fb79dbd7c2c0e3f5b85d2b326518da0b78e149a01222f0024e62dd870"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OiaUmTHyraGhlueVglUaDzSOJP8f01ahwzBwAxPL1HeNPjayVCcZTzoUh3HBSl2BVWhqMXI4Ee0myzFXO7EGDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:18:36.807157Z","bundle_sha256":"90df4171a21ac93c74920888f3206446ffe385d2ef9f047750b9ebdb88cfeaf9"}}