{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:PYXAOXKXYL6EEJG2T23RZVP3LM","short_pith_number":"pith:PYXAOXKX","canonical_record":{"source":{"id":"2305.01204","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-05-02T05:03:53Z","cross_cats_sorted":[],"title_canon_sha256":"4729f86ebf9b3bf1fb538580d3615bea370496a43a47a63e7899039e2f1c69d9","abstract_canon_sha256":"238b23e089215602a07284bb16e9c6c67ddb87015ce8383486bf43de7748cd33"},"schema_version":"1.0"},"canonical_sha256":"7e2e075d57c2fc4224da9eb71cd5fb5b0117e86f49d42447cb64cfd23319dfe0","source":{"kind":"arxiv","id":"2305.01204","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.01204","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"arxiv_version","alias_value":"2305.01204v1","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01204","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"pith_short_12","alias_value":"PYXAOXKXYL6E","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"pith_short_16","alias_value":"PYXAOXKXYL6EEJG2","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"pith_short_8","alias_value":"PYXAOXKX","created_at":"2026-07-05T06:06:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:PYXAOXKXYL6EEJG2T23RZVP3LM","target":"record","payload":{"canonical_record":{"source":{"id":"2305.01204","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-05-02T05:03:53Z","cross_cats_sorted":[],"title_canon_sha256":"4729f86ebf9b3bf1fb538580d3615bea370496a43a47a63e7899039e2f1c69d9","abstract_canon_sha256":"238b23e089215602a07284bb16e9c6c67ddb87015ce8383486bf43de7748cd33"},"schema_version":"1.0"},"canonical_sha256":"7e2e075d57c2fc4224da9eb71cd5fb5b0117e86f49d42447cb64cfd23319dfe0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:22.837625Z","signature_b64":"2D7v2lzG3IhSeTQfShtn8Z/lA09UvukKyOUm8htSiVcwwxkDmQOUS0gCLSvSs+HjQOx8MPmFOJZ7Sh4Gsl3ZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e2e075d57c2fc4224da9eb71cd5fb5b0117e86f49d42447cb64cfd23319dfe0","last_reissued_at":"2026-07-05T06:06:22.837124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:22.837124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.01204","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":"2026-07-05T06:06:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KECgUKTvJGPBCb+lMq33HOmbbZO3xqo+9yXmpxJKi6dw33mglLI/e/j5cueZU2P8m+OIhGolpPJh2qK7EK4jCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:44:50.581863Z"},"content_sha256":"906729d3ab9609cfa783e448af43e7fbbaa1bf54efea5891ea43d261b3317062","schema_version":"1.0","event_id":"sha256:906729d3ab9609cfa783e448af43e7fbbaa1bf54efea5891ea43d261b3317062"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:PYXAOXKXYL6EEJG2T23RZVP3LM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Antonios Valkanas, Chen Ma, Jianye Hao, Mark Coates, Ruiming Tang, Yingxue Zhang, Yuening Wang","submitted_at":"2023-05-02T05:03:53Z","abstract_excerpt":"Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model the rich relational information. The ever-growing volume of data can make training GNNs prohibitively expensive. To address this, previous attempts propose to train the GNN models incrementally as new data blocks arrive. Feature and structure knowledge distillation techniques have been explored to allow the GNN model to train in a fast incremental fashion while alleviating t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01204","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/2305.01204/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":"2026-07-05T06:06:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q79Mxtr0e+Puzx+QvxCQ4Wc9Ypwv3YeK3P6zySfEs1ffkIcmsYsbVlMDwX18Su1sDDsBCzhDfdMX2UW5TVIICw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:44:50.582436Z"},"content_sha256":"f2e671f3910c94f2772a3f5c78de1bca0b0ca46c1fa17418a1920cc564a382ae","schema_version":"1.0","event_id":"sha256:f2e671f3910c94f2772a3f5c78de1bca0b0ca46c1fa17418a1920cc564a382ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PYXAOXKXYL6EEJG2T23RZVP3LM/bundle.json","state_url":"https://pith.science/pith/PYXAOXKXYL6EEJG2T23RZVP3LM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PYXAOXKXYL6EEJG2T23RZVP3LM/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":"2026-08-04T12:44:50Z","links":{"resolver":"https://pith.science/pith/PYXAOXKXYL6EEJG2T23RZVP3LM","bundle":"https://pith.science/pith/PYXAOXKXYL6EEJG2T23RZVP3LM/bundle.json","state":"https://pith.science/pith/PYXAOXKXYL6EEJG2T23RZVP3LM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PYXAOXKXYL6EEJG2T23RZVP3LM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PYXAOXKXYL6EEJG2T23RZVP3LM","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":"238b23e089215602a07284bb16e9c6c67ddb87015ce8383486bf43de7748cd33","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-05-02T05:03:53Z","title_canon_sha256":"4729f86ebf9b3bf1fb538580d3615bea370496a43a47a63e7899039e2f1c69d9"},"schema_version":"1.0","source":{"id":"2305.01204","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.01204","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"arxiv_version","alias_value":"2305.01204v1","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01204","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"pith_short_12","alias_value":"PYXAOXKXYL6E","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"pith_short_16","alias_value":"PYXAOXKXYL6EEJG2","created_at":"2026-07-05T06:06:22Z"},{"alias_kind":"pith_short_8","alias_value":"PYXAOXKX","created_at":"2026-07-05T06:06:22Z"}],"graph_snapshots":[{"event_id":"sha256:f2e671f3910c94f2772a3f5c78de1bca0b0ca46c1fa17418a1920cc564a382ae","target":"graph","created_at":"2026-07-05T06:06:22Z","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/2305.01204/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model the rich relational information. The ever-growing volume of data can make training GNNs prohibitively expensive. To address this, previous attempts propose to train the GNN models incrementally as new data blocks arrive. Feature and structure knowledge distillation techniques have been explored to allow the GNN model to train in a fast incremental fashion while alleviating t","authors_text":"Antonios Valkanas, Chen Ma, Jianye Hao, Mark Coates, Ruiming Tang, Yingxue Zhang, Yuening Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-05-02T05:03:53Z","title":"Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01204","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":"sha256:906729d3ab9609cfa783e448af43e7fbbaa1bf54efea5891ea43d261b3317062","target":"record","created_at":"2026-07-05T06:06:22Z","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":"238b23e089215602a07284bb16e9c6c67ddb87015ce8383486bf43de7748cd33","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-05-02T05:03:53Z","title_canon_sha256":"4729f86ebf9b3bf1fb538580d3615bea370496a43a47a63e7899039e2f1c69d9"},"schema_version":"1.0","source":{"id":"2305.01204","kind":"arxiv","version":1}},"canonical_sha256":"7e2e075d57c2fc4224da9eb71cd5fb5b0117e86f49d42447cb64cfd23319dfe0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7e2e075d57c2fc4224da9eb71cd5fb5b0117e86f49d42447cb64cfd23319dfe0","first_computed_at":"2026-07-05T06:06:22.837124Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:06:22.837124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2D7v2lzG3IhSeTQfShtn8Z/lA09UvukKyOUm8htSiVcwwxkDmQOUS0gCLSvSs+HjQOx8MPmFOJZ7Sh4Gsl3ZBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:06:22.837625Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.01204","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:906729d3ab9609cfa783e448af43e7fbbaa1bf54efea5891ea43d261b3317062","sha256:f2e671f3910c94f2772a3f5c78de1bca0b0ca46c1fa17418a1920cc564a382ae"],"state_sha256":"d7e58e809f029d2649c1e645d73dc60fce643386728cc8c86576027e09d8bd1e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M5lVkxztoRBYEkHwSKpkGlgu59n3nuh4yiu7gNSaz8JF5kgmWoJzKrx44y6MxknevvqKwBaE0V4Y1Zkt+LVgAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:44:50.586372Z","bundle_sha256":"44a12e7f55eea315208419971bdbfb709fdd3f292a9c9ea84badfff7ead2e9e2"}}