{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RZM6AHUSARVO26Q5DURNBFXT7D","short_pith_number":"pith:RZM6AHUS","canonical_record":{"source":{"id":"2302.00219","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T03:51:05Z","cross_cats_sorted":[],"title_canon_sha256":"3baea28df2ae5ffda098de931744023f277850fcca9c4b78eed5dd8b0576c6e2","abstract_canon_sha256":"1b011982846122971693a8c82db25899b409233eda4a6a1debf631dae394d4db"},"schema_version":"1.0"},"canonical_sha256":"8e59e01e92046aed7a1d1d22d096f3f8ccff818cdb268d64c99e05fe208c473c","source":{"kind":"arxiv","id":"2302.00219","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.00219","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"arxiv_version","alias_value":"2302.00219v1","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.00219","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_12","alias_value":"RZM6AHUSARVO","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_16","alias_value":"RZM6AHUSARVO26Q5","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_8","alias_value":"RZM6AHUS","created_at":"2026-07-05T05:38:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RZM6AHUSARVO26Q5DURNBFXT7D","target":"record","payload":{"canonical_record":{"source":{"id":"2302.00219","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T03:51:05Z","cross_cats_sorted":[],"title_canon_sha256":"3baea28df2ae5ffda098de931744023f277850fcca9c4b78eed5dd8b0576c6e2","abstract_canon_sha256":"1b011982846122971693a8c82db25899b409233eda4a6a1debf631dae394d4db"},"schema_version":"1.0"},"canonical_sha256":"8e59e01e92046aed7a1d1d22d096f3f8ccff818cdb268d64c99e05fe208c473c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:38:00.989762Z","signature_b64":"KNWfWiOLHZFBjrMoGbOxJVYgTfKtbx23ilMsJGVMPwtdRRjoospOG9H+dOhSZzBN/MCQT+7izPlQnFHH5ZUbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e59e01e92046aed7a1d1d22d096f3f8ccff818cdb268d64c99e05fe208c473c","last_reissued_at":"2026-07-05T05:38:00.989309Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:38:00.989309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.00219","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":"2026-07-05T05:38:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s+BajIB5CwgfBIBW5sY96cxAgdMXivXgEA+BWpvqpiVeO78Pb3cCs82wRdupMSs9iMys2csFU4exz9rT/RFCDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T20:46:16.207814Z"},"content_sha256":"6dce9970e0770054c297a67871b0fde191253d9e920e1f855c51c91605be1371","schema_version":"1.0","event_id":"sha256:6dce9970e0770054c297a67871b0fde191253d9e920e1f855c51c91605be1371"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RZM6AHUSARVO26Q5DURNBFXT7D","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Knowledge Distillation on Graphs: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chuxu Zhang, Nitesh V. Chawla, Shichao Pei, Xiangliang Zhang, Yijun Tian","submitted_at":"2023-02-01T03:51:05Z","abstract_excerpt":"Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limited devices due to model sizes and scalability constraints imposed by the multi-hop data dependency. In addition, real-world graphs usually possess complex structural information and features. Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, knowledge distillation on graphs (KDG) has been introduced to build a smaller yet effective model and exploit more knowle"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.00219","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/2302.00219/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":"2026-07-05T05:38:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZU+UUfhjnANtD44wKlpoqskE2gRk3UBnSRFZg7scw3SPRRf6PLw9bbjx0uYZJAdFlNA97GOVwtsBqvew8YHbBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T20:46:16.208329Z"},"content_sha256":"9e66e85cd542842fa4fb1d6ecc64266d1de7b7954fde3d8ae84288125a79ddfb","schema_version":"1.0","event_id":"sha256:9e66e85cd542842fa4fb1d6ecc64266d1de7b7954fde3d8ae84288125a79ddfb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RZM6AHUSARVO26Q5DURNBFXT7D/bundle.json","state_url":"https://pith.science/pith/RZM6AHUSARVO26Q5DURNBFXT7D/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RZM6AHUSARVO26Q5DURNBFXT7D/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":"2026-08-15T20:46:16Z","links":{"resolver":"https://pith.science/pith/RZM6AHUSARVO26Q5DURNBFXT7D","bundle":"https://pith.science/pith/RZM6AHUSARVO26Q5DURNBFXT7D/bundle.json","state":"https://pith.science/pith/RZM6AHUSARVO26Q5DURNBFXT7D/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RZM6AHUSARVO26Q5DURNBFXT7D/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RZM6AHUSARVO26Q5DURNBFXT7D","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":"1b011982846122971693a8c82db25899b409233eda4a6a1debf631dae394d4db","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T03:51:05Z","title_canon_sha256":"3baea28df2ae5ffda098de931744023f277850fcca9c4b78eed5dd8b0576c6e2"},"schema_version":"1.0","source":{"id":"2302.00219","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.00219","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"arxiv_version","alias_value":"2302.00219v1","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.00219","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_12","alias_value":"RZM6AHUSARVO","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_16","alias_value":"RZM6AHUSARVO26Q5","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_8","alias_value":"RZM6AHUS","created_at":"2026-07-05T05:38:00Z"}],"graph_snapshots":[{"event_id":"sha256:9e66e85cd542842fa4fb1d6ecc64266d1de7b7954fde3d8ae84288125a79ddfb","target":"graph","created_at":"2026-07-05T05:38:00Z","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/2302.00219/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limited devices due to model sizes and scalability constraints imposed by the multi-hop data dependency. In addition, real-world graphs usually possess complex structural information and features. Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, knowledge distillation on graphs (KDG) has been introduced to build a smaller yet effective model and exploit more knowle","authors_text":"Chuxu Zhang, Nitesh V. Chawla, Shichao Pei, Xiangliang Zhang, Yijun Tian","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T03:51:05Z","title":"Knowledge Distillation on Graphs: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.00219","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":"sha256:6dce9970e0770054c297a67871b0fde191253d9e920e1f855c51c91605be1371","target":"record","created_at":"2026-07-05T05:38:00Z","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":"1b011982846122971693a8c82db25899b409233eda4a6a1debf631dae394d4db","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T03:51:05Z","title_canon_sha256":"3baea28df2ae5ffda098de931744023f277850fcca9c4b78eed5dd8b0576c6e2"},"schema_version":"1.0","source":{"id":"2302.00219","kind":"arxiv","version":1}},"canonical_sha256":"8e59e01e92046aed7a1d1d22d096f3f8ccff818cdb268d64c99e05fe208c473c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8e59e01e92046aed7a1d1d22d096f3f8ccff818cdb268d64c99e05fe208c473c","first_computed_at":"2026-07-05T05:38:00.989309Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:38:00.989309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KNWfWiOLHZFBjrMoGbOxJVYgTfKtbx23ilMsJGVMPwtdRRjoospOG9H+dOhSZzBN/MCQT+7izPlQnFHH5ZUbDw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:38:00.989762Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.00219","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6dce9970e0770054c297a67871b0fde191253d9e920e1f855c51c91605be1371","sha256:9e66e85cd542842fa4fb1d6ecc64266d1de7b7954fde3d8ae84288125a79ddfb"],"state_sha256":"a71eeffc093467e6a8faee623cfae9ac7cc18c7863f239c9fbc93e5252f11a89"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+VtBo4X3Lgcc/Um171AZAgtkh5kCWGLMe9ih2na6SXRYPFKD3kHJ9cJr+mOpfZKOUN2Fvtn0TcyI8qEQ5Yk/Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T20:46:16.212476Z","bundle_sha256":"6f02b05e4877cbc47999b14f11f788c01163bdb8e634d1bba5331d7671da8e2e"}}