{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:D3PVDJCJNMD5ER7QCOBLBZTINM","short_pith_number":"pith:D3PVDJCJ","canonical_record":{"source":{"id":"2204.12881","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T12:38:02Z","cross_cats_sorted":[],"title_canon_sha256":"608be873bc0a6bce227c5c867108fd4754f7ebe41b729b465892be201cb5b9b4","abstract_canon_sha256":"05e0e869b5cff6d1e9134581d9ea31d5e6f49b85f0c37bbde9b543c3c21ad28b"},"schema_version":"1.0"},"canonical_sha256":"1edf51a4496b07d247f01382b0e6686b159b8bdcfb16a26e10d85e9617d08ace","source":{"kind":"arxiv","id":"2204.12881","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.12881","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"arxiv_version","alias_value":"2204.12881v1","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.12881","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"pith_short_12","alias_value":"D3PVDJCJNMD5","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"pith_short_16","alias_value":"D3PVDJCJNMD5ER7Q","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"pith_short_8","alias_value":"D3PVDJCJ","created_at":"2026-07-05T04:18:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:D3PVDJCJNMD5ER7QCOBLBZTINM","target":"record","payload":{"canonical_record":{"source":{"id":"2204.12881","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T12:38:02Z","cross_cats_sorted":[],"title_canon_sha256":"608be873bc0a6bce227c5c867108fd4754f7ebe41b729b465892be201cb5b9b4","abstract_canon_sha256":"05e0e869b5cff6d1e9134581d9ea31d5e6f49b85f0c37bbde9b543c3c21ad28b"},"schema_version":"1.0"},"canonical_sha256":"1edf51a4496b07d247f01382b0e6686b159b8bdcfb16a26e10d85e9617d08ace","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:18:23.820654Z","signature_b64":"8dHZPdtIA7DCAA8uJ+/+RF5FzYfGKULGofs69D3EFAez8QilQiukg9HhvgbAww/B4C2umvE3tJKYYpKMhOWDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1edf51a4496b07d247f01382b0e6686b159b8bdcfb16a26e10d85e9617d08ace","last_reissued_at":"2026-07-05T04:18:23.820259Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:18:23.820259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.12881","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":"2026-07-05T04:18:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sOBAelBg/bopinkRXjogXEzr3ZGGR9jqkpssj12TzlCGu4o5j3myG09L4bChnX7hqC61U0DMsTk+lTOiZ4amAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:23:38.425038Z"},"content_sha256":"e13edd6045b71c3854773c0b874c1dc9eb47c170d8a8017b97f3741c4dc3c3eb","schema_version":"1.0","event_id":"sha256:e13edd6045b71c3854773c0b874c1dc9eb47c170d8a8017b97f3741c4dc3c3eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:D3PVDJCJNMD5ER7QCOBLBZTINM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chenglin Li, Hongkai Xiong, Junni Zou, Mingxing Xu, Wenrui Dai","submitted_at":"2022-04-27T12:38:02Z","abstract_excerpt":"Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages, i.e., selecting the top-ranked nodes and removing the rest nodes to construct a coarsened graph representation. However, local structural information of the removed nodes would be inevitably dropped in these methods, due to the inherent coupling of nodes (location) and their features (signals). In this paper, we propose an enhanced three-stage method via lifting, named LiftPool, to improve hierar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.12881","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/2204.12881/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":"2026-07-05T04:18:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"urzva1G/G4gZJNewOZ9dA1t3zkur6cxtm1EaxGjwX4tyLx/q3INRtu6JoaX8V+nLxiakmjIQR+5j3YEs5DcrBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:23:38.425623Z"},"content_sha256":"182e8003f583744615b9ef51e6e0f195c50fdc617b63001f9f6931ff53c8fd2a","schema_version":"1.0","event_id":"sha256:182e8003f583744615b9ef51e6e0f195c50fdc617b63001f9f6931ff53c8fd2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D3PVDJCJNMD5ER7QCOBLBZTINM/bundle.json","state_url":"https://pith.science/pith/D3PVDJCJNMD5ER7QCOBLBZTINM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D3PVDJCJNMD5ER7QCOBLBZTINM/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":"2026-08-09T00:23:38Z","links":{"resolver":"https://pith.science/pith/D3PVDJCJNMD5ER7QCOBLBZTINM","bundle":"https://pith.science/pith/D3PVDJCJNMD5ER7QCOBLBZTINM/bundle.json","state":"https://pith.science/pith/D3PVDJCJNMD5ER7QCOBLBZTINM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D3PVDJCJNMD5ER7QCOBLBZTINM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:D3PVDJCJNMD5ER7QCOBLBZTINM","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":"05e0e869b5cff6d1e9134581d9ea31d5e6f49b85f0c37bbde9b543c3c21ad28b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T12:38:02Z","title_canon_sha256":"608be873bc0a6bce227c5c867108fd4754f7ebe41b729b465892be201cb5b9b4"},"schema_version":"1.0","source":{"id":"2204.12881","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.12881","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"arxiv_version","alias_value":"2204.12881v1","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.12881","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"pith_short_12","alias_value":"D3PVDJCJNMD5","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"pith_short_16","alias_value":"D3PVDJCJNMD5ER7Q","created_at":"2026-07-05T04:18:23Z"},{"alias_kind":"pith_short_8","alias_value":"D3PVDJCJ","created_at":"2026-07-05T04:18:23Z"}],"graph_snapshots":[{"event_id":"sha256:182e8003f583744615b9ef51e6e0f195c50fdc617b63001f9f6931ff53c8fd2a","target":"graph","created_at":"2026-07-05T04:18:23Z","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/2204.12881/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages, i.e., selecting the top-ranked nodes and removing the rest nodes to construct a coarsened graph representation. However, local structural information of the removed nodes would be inevitably dropped in these methods, due to the inherent coupling of nodes (location) and their features (signals). In this paper, we propose an enhanced three-stage method via lifting, named LiftPool, to improve hierar","authors_text":"Chenglin Li, Hongkai Xiong, Junni Zou, Mingxing Xu, Wenrui Dai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T12:38:02Z","title":"LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.12881","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:e13edd6045b71c3854773c0b874c1dc9eb47c170d8a8017b97f3741c4dc3c3eb","target":"record","created_at":"2026-07-05T04:18:23Z","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":"05e0e869b5cff6d1e9134581d9ea31d5e6f49b85f0c37bbde9b543c3c21ad28b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-27T12:38:02Z","title_canon_sha256":"608be873bc0a6bce227c5c867108fd4754f7ebe41b729b465892be201cb5b9b4"},"schema_version":"1.0","source":{"id":"2204.12881","kind":"arxiv","version":1}},"canonical_sha256":"1edf51a4496b07d247f01382b0e6686b159b8bdcfb16a26e10d85e9617d08ace","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1edf51a4496b07d247f01382b0e6686b159b8bdcfb16a26e10d85e9617d08ace","first_computed_at":"2026-07-05T04:18:23.820259Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:18:23.820259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8dHZPdtIA7DCAA8uJ+/+RF5FzYfGKULGofs69D3EFAez8QilQiukg9HhvgbAww/B4C2umvE3tJKYYpKMhOWDBw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:18:23.820654Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.12881","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e13edd6045b71c3854773c0b874c1dc9eb47c170d8a8017b97f3741c4dc3c3eb","sha256:182e8003f583744615b9ef51e6e0f195c50fdc617b63001f9f6931ff53c8fd2a"],"state_sha256":"fb737073cd6df246997304e5d8c451afb34d0a56ebde894e248b6a5f56783d28"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"opTKtEiyyQwZxHEPiU7DJk9YQxAaB6I3RIk/LVa+wg9SGpYELewu9rXq26NYiyoSoMwqcnJJzsKd/3Lre4dUCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T00:23:38.430500Z","bundle_sha256":"17e9fd5dc2e6df5746bf1f7d3b0cfde756991678196be6f51521d3665aa26d32"}}