{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:DJC34PYRB3XCOFQ3VEXNRDA7AN","short_pith_number":"pith:DJC34PYR","canonical_record":{"source":{"id":"2402.04296","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T14:40:26Z","cross_cats_sorted":[],"title_canon_sha256":"c17923111a559544351abdd444bc89e9f62f6acd64c24fa11c51c46e455a7cc7","abstract_canon_sha256":"904aa5fde925f49d402343c31cde3fc866e5ceb8f783278d8d497493e13f1ae8"},"schema_version":"1.0"},"canonical_sha256":"1a45be3f110eee27161ba92ed88c1f0359fa5f179cd43ee42fb099e3abda1f34","source":{"kind":"arxiv","id":"2402.04296","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.04296","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"arxiv_version","alias_value":"2402.04296v2","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.04296","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"pith_short_12","alias_value":"DJC34PYRB3XC","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"pith_short_16","alias_value":"DJC34PYRB3XCOFQ3","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"pith_short_8","alias_value":"DJC34PYR","created_at":"2026-07-05T07:46:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:DJC34PYRB3XCOFQ3VEXNRDA7AN","target":"record","payload":{"canonical_record":{"source":{"id":"2402.04296","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T14:40:26Z","cross_cats_sorted":[],"title_canon_sha256":"c17923111a559544351abdd444bc89e9f62f6acd64c24fa11c51c46e455a7cc7","abstract_canon_sha256":"904aa5fde925f49d402343c31cde3fc866e5ceb8f783278d8d497493e13f1ae8"},"schema_version":"1.0"},"canonical_sha256":"1a45be3f110eee27161ba92ed88c1f0359fa5f179cd43ee42fb099e3abda1f34","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:31.063748Z","signature_b64":"DV3271FrSlftP6UwM0N9vwB0jT4iINC2kUSolsk2AgwTMEAak2xe9Yy6v3mdViZdoeOvgBZVAjWWckWCXGk5DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a45be3f110eee27161ba92ed88c1f0359fa5f179cd43ee42fb099e3abda1f34","last_reissued_at":"2026-07-05T07:46:31.063236Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:31.063236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.04296","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:46:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vMq7sTWPZcuwj+q5QfXCVJYxYPJmxCmGWmwOaAx/5viMKppbW7Y/9W32eCO3xkl7je2NslZHL317e+EdkmRkCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T07:41:24.234875Z"},"content_sha256":"7526846e8e1e26fcc3cddd20a0e285cb9a842c117a33a6f92465f2dc4c6a8627","schema_version":"1.0","event_id":"sha256:7526846e8e1e26fcc3cddd20a0e285cb9a842c117a33a6f92465f2dc4c6a8627"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:DJC34PYRB3XCOFQ3VEXNRDA7AN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LightHGNN: Distilling Hypergraph Neural Networks into MLPs for $100\\times$ Faster Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Shihui Ying, Yifan Feng, Yihe Luo, Yue Gao","submitted_at":"2024-02-06T14:40:26Z","abstract_excerpt":"Hypergraph Neural Networks (HGNNs) have recently attracted much attention and exhibited satisfactory performance due to their superiority in high-order correlation modeling. However, it is noticed that the high-order modeling capability of hypergraph also brings increased computation complexity, which hinders its practical industrial deployment. In practice, we find that one key barrier to the efficient deployment of HGNNs is the high-order structural dependencies during inference. In this paper, we propose to bridge the gap between the HGNNs and inference-efficient Multi-Layer Perceptron (MLP"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.04296","kind":"arxiv","version":2},"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/2402.04296/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":"2026-07-05T07:46:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NratCGKsMs3zX4EYaREFZ3C9FFc0zbehgQOtN4/uhyOL4fRtkLCnYH+/jOnQxpCUqnilWNmhGrhY1ulEvs95AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T07:41:24.235700Z"},"content_sha256":"bc17a5ba523af5355dbcfef35c1072a3e7b10d06bb9de03666fe299a4e1cc0a5","schema_version":"1.0","event_id":"sha256:bc17a5ba523af5355dbcfef35c1072a3e7b10d06bb9de03666fe299a4e1cc0a5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DJC34PYRB3XCOFQ3VEXNRDA7AN/bundle.json","state_url":"https://pith.science/pith/DJC34PYRB3XCOFQ3VEXNRDA7AN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DJC34PYRB3XCOFQ3VEXNRDA7AN/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":"2026-08-01T07:41:24Z","links":{"resolver":"https://pith.science/pith/DJC34PYRB3XCOFQ3VEXNRDA7AN","bundle":"https://pith.science/pith/DJC34PYRB3XCOFQ3VEXNRDA7AN/bundle.json","state":"https://pith.science/pith/DJC34PYRB3XCOFQ3VEXNRDA7AN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DJC34PYRB3XCOFQ3VEXNRDA7AN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DJC34PYRB3XCOFQ3VEXNRDA7AN","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":"904aa5fde925f49d402343c31cde3fc866e5ceb8f783278d8d497493e13f1ae8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T14:40:26Z","title_canon_sha256":"c17923111a559544351abdd444bc89e9f62f6acd64c24fa11c51c46e455a7cc7"},"schema_version":"1.0","source":{"id":"2402.04296","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.04296","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"arxiv_version","alias_value":"2402.04296v2","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.04296","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"pith_short_12","alias_value":"DJC34PYRB3XC","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"pith_short_16","alias_value":"DJC34PYRB3XCOFQ3","created_at":"2026-07-05T07:46:31Z"},{"alias_kind":"pith_short_8","alias_value":"DJC34PYR","created_at":"2026-07-05T07:46:31Z"}],"graph_snapshots":[{"event_id":"sha256:bc17a5ba523af5355dbcfef35c1072a3e7b10d06bb9de03666fe299a4e1cc0a5","target":"graph","created_at":"2026-07-05T07:46:31Z","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/2402.04296/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hypergraph Neural Networks (HGNNs) have recently attracted much attention and exhibited satisfactory performance due to their superiority in high-order correlation modeling. However, it is noticed that the high-order modeling capability of hypergraph also brings increased computation complexity, which hinders its practical industrial deployment. In practice, we find that one key barrier to the efficient deployment of HGNNs is the high-order structural dependencies during inference. In this paper, we propose to bridge the gap between the HGNNs and inference-efficient Multi-Layer Perceptron (MLP","authors_text":"Shihui Ying, Yifan Feng, Yihe Luo, Yue Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T14:40:26Z","title":"LightHGNN: Distilling Hypergraph Neural Networks into MLPs for $100\\times$ Faster Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.04296","kind":"arxiv","version":2},"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":"sha256:7526846e8e1e26fcc3cddd20a0e285cb9a842c117a33a6f92465f2dc4c6a8627","target":"record","created_at":"2026-07-05T07:46:31Z","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":"904aa5fde925f49d402343c31cde3fc866e5ceb8f783278d8d497493e13f1ae8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T14:40:26Z","title_canon_sha256":"c17923111a559544351abdd444bc89e9f62f6acd64c24fa11c51c46e455a7cc7"},"schema_version":"1.0","source":{"id":"2402.04296","kind":"arxiv","version":2}},"canonical_sha256":"1a45be3f110eee27161ba92ed88c1f0359fa5f179cd43ee42fb099e3abda1f34","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a45be3f110eee27161ba92ed88c1f0359fa5f179cd43ee42fb099e3abda1f34","first_computed_at":"2026-07-05T07:46:31.063236Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:46:31.063236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DV3271FrSlftP6UwM0N9vwB0jT4iINC2kUSolsk2AgwTMEAak2xe9Yy6v3mdViZdoeOvgBZVAjWWckWCXGk5DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:46:31.063748Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.04296","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7526846e8e1e26fcc3cddd20a0e285cb9a842c117a33a6f92465f2dc4c6a8627","sha256:bc17a5ba523af5355dbcfef35c1072a3e7b10d06bb9de03666fe299a4e1cc0a5"],"state_sha256":"42b03ed59dc7053e68f7bcb941cedafc47ac704d84e1dd020cd83159c854336a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HPAofM3iWMmy+mvRQOnB9lChV2cQBQQdkR0g5x88KTQX7fkfh4VkUzwNl3K/KuKBruN+L10K29oTwsUI8JFpCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T07:41:24.242016Z","bundle_sha256":"f58bd6e82b3f2458a8e0095ebde28d5b3d3ad58497307fac1f439939d171c96e"}}