{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:P7UB6TVH5TQVA5XWY4NWMUBWVI","short_pith_number":"pith:P7UB6TVH","canonical_record":{"source":{"id":"2507.19031","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-25T07:35:09Z","cross_cats_sorted":[],"title_canon_sha256":"d17491910089538bfd71340b83a2293f62cf3cb0b73666dd795787caa7d791ba","abstract_canon_sha256":"e75b87f8c5d5450d8aa76a8c8eb22c05ccf33bacf33f966db0ee0e8614631742"},"schema_version":"1.0"},"canonical_sha256":"7fe81f4ea7ece15076f6c71b665036aa200bf0a35ae9f6eb457ed764fff8a840","source":{"kind":"arxiv","id":"2507.19031","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.19031","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"2507.19031v1","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.19031","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"P7UB6TVH5TQV","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"pith_short_16","alias_value":"P7UB6TVH5TQVA5XW","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"pith_short_8","alias_value":"P7UB6TVH","created_at":"2026-07-05T11:43:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:P7UB6TVH5TQVA5XWY4NWMUBWVI","target":"record","payload":{"canonical_record":{"source":{"id":"2507.19031","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-25T07:35:09Z","cross_cats_sorted":[],"title_canon_sha256":"d17491910089538bfd71340b83a2293f62cf3cb0b73666dd795787caa7d791ba","abstract_canon_sha256":"e75b87f8c5d5450d8aa76a8c8eb22c05ccf33bacf33f966db0ee0e8614631742"},"schema_version":"1.0"},"canonical_sha256":"7fe81f4ea7ece15076f6c71b665036aa200bf0a35ae9f6eb457ed764fff8a840","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:18.278880Z","signature_b64":"FMGrWGkEAOxyokhbyf9dmtvq/1MiUAgL2OnRYj+y6vVkBSX8TZda2wK5kdCB8rsK2QE801MxY6nbF0CdJ+yBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7fe81f4ea7ece15076f6c71b665036aa200bf0a35ae9f6eb457ed764fff8a840","last_reissued_at":"2026-07-05T11:43:18.277461Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:18.277461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.19031","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":"2026-07-05T11:43:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mGgI0bJPn15bRSCxZ0cD4GQdg9nUVF1L66FsGyzvXU0IYh7yePKxOp7Xnd7VOfR35lZGdkN+OSGI3yw/Ta2xDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:47:32.162720Z"},"content_sha256":"68fcb622b7cbc77b1d75efbd954598a001f59619f1c480dfcaf76302aee73536","schema_version":"1.0","event_id":"sha256:68fcb622b7cbc77b1d75efbd954598a001f59619f1c480dfcaf76302aee73536"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:P7UB6TVH5TQVA5XWY4NWMUBWVI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dapeng Tao, Weigang Lu, Wei Zhao, Yaming Yang, Yibing Zhan, Yujie Sun, Zheng Liang, Ziyu Guan","submitted_at":"2025-07-25T07:35:09Z","abstract_excerpt":"GNN-to-MLP (G2M) methods have emerged as a promising approach to accelerate Graph Neural Networks (GNNs) by distilling their knowledge into simpler Multi-Layer Perceptrons (MLPs). These methods bridge the gap between the expressive power of GNNs and the computational efficiency of MLPs, making them well-suited for resource-constrained environments. However, existing G2M methods are limited by their inability to flexibly adjust inference cost and accuracy dynamically, a critical requirement for real-world applications where computational resources and time constraints can vary significantly. To"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.19031","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/2507.19031/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":"2026-07-05T11:43:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WshN+hNK5/t+eltWvTjbomwz9VpZ5aX0QEqdP0oOi5eCeC+bsOObD9evMLVwhVVO+IqI786HnlqF91wrbEsSAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:47:32.163354Z"},"content_sha256":"e4e98690872354dba683b3b93b8b4d8f1a68ccaa7793fd8657f723c2b213915d","schema_version":"1.0","event_id":"sha256:e4e98690872354dba683b3b93b8b4d8f1a68ccaa7793fd8657f723c2b213915d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P7UB6TVH5TQVA5XWY4NWMUBWVI/bundle.json","state_url":"https://pith.science/pith/P7UB6TVH5TQVA5XWY4NWMUBWVI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P7UB6TVH5TQVA5XWY4NWMUBWVI/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":"2026-08-17T08:47:32Z","links":{"resolver":"https://pith.science/pith/P7UB6TVH5TQVA5XWY4NWMUBWVI","bundle":"https://pith.science/pith/P7UB6TVH5TQVA5XWY4NWMUBWVI/bundle.json","state":"https://pith.science/pith/P7UB6TVH5TQVA5XWY4NWMUBWVI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P7UB6TVH5TQVA5XWY4NWMUBWVI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:P7UB6TVH5TQVA5XWY4NWMUBWVI","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":"e75b87f8c5d5450d8aa76a8c8eb22c05ccf33bacf33f966db0ee0e8614631742","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-25T07:35:09Z","title_canon_sha256":"d17491910089538bfd71340b83a2293f62cf3cb0b73666dd795787caa7d791ba"},"schema_version":"1.0","source":{"id":"2507.19031","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.19031","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"2507.19031v1","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.19031","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"P7UB6TVH5TQV","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"pith_short_16","alias_value":"P7UB6TVH5TQVA5XW","created_at":"2026-07-05T11:43:18Z"},{"alias_kind":"pith_short_8","alias_value":"P7UB6TVH","created_at":"2026-07-05T11:43:18Z"}],"graph_snapshots":[{"event_id":"sha256:e4e98690872354dba683b3b93b8b4d8f1a68ccaa7793fd8657f723c2b213915d","target":"graph","created_at":"2026-07-05T11:43:18Z","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/2507.19031/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"GNN-to-MLP (G2M) methods have emerged as a promising approach to accelerate Graph Neural Networks (GNNs) by distilling their knowledge into simpler Multi-Layer Perceptrons (MLPs). These methods bridge the gap between the expressive power of GNNs and the computational efficiency of MLPs, making them well-suited for resource-constrained environments. However, existing G2M methods are limited by their inability to flexibly adjust inference cost and accuracy dynamically, a critical requirement for real-world applications where computational resources and time constraints can vary significantly. To","authors_text":"Dapeng Tao, Weigang Lu, Wei Zhao, Yaming Yang, Yibing Zhan, Yujie Sun, Zheng Liang, Ziyu Guan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-25T07:35:09Z","title":"ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.19031","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":"sha256:68fcb622b7cbc77b1d75efbd954598a001f59619f1c480dfcaf76302aee73536","target":"record","created_at":"2026-07-05T11:43:18Z","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":"e75b87f8c5d5450d8aa76a8c8eb22c05ccf33bacf33f966db0ee0e8614631742","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-25T07:35:09Z","title_canon_sha256":"d17491910089538bfd71340b83a2293f62cf3cb0b73666dd795787caa7d791ba"},"schema_version":"1.0","source":{"id":"2507.19031","kind":"arxiv","version":1}},"canonical_sha256":"7fe81f4ea7ece15076f6c71b665036aa200bf0a35ae9f6eb457ed764fff8a840","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7fe81f4ea7ece15076f6c71b665036aa200bf0a35ae9f6eb457ed764fff8a840","first_computed_at":"2026-07-05T11:43:18.277461Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:43:18.277461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FMGrWGkEAOxyokhbyf9dmtvq/1MiUAgL2OnRYj+y6vVkBSX8TZda2wK5kdCB8rsK2QE801MxY6nbF0CdJ+yBCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:43:18.278880Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.19031","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68fcb622b7cbc77b1d75efbd954598a001f59619f1c480dfcaf76302aee73536","sha256:e4e98690872354dba683b3b93b8b4d8f1a68ccaa7793fd8657f723c2b213915d"],"state_sha256":"37c16551d90a898d5984a04cf345b6e316db3ee70fe47b84b105a2ecf4fe4cb4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jbD2gZ4kg+yHpvgQq5DrQwsU/CwPJciMf8cQQAONvVHSi/9mKB2tubnNIG90L+zUWqO8X/icAdUwn3/n2mlkCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T08:47:32.167320Z","bundle_sha256":"c016d57d5b8e76a79c302fb3a0712f8ea4f51b6d82cff8f8dab62752fce86db3"}}