{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GUKZBW7WSTTZUEOHGIHBU6TF5T","short_pith_number":"pith:GUKZBW7W","canonical_record":{"source":{"id":"2303.06147","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-10T18:59:57Z","cross_cats_sorted":[],"title_canon_sha256":"3a161627a2b4430300589ce9ce38b5a876a0ff2db5b6bfdcb4da0a693abdb61a","abstract_canon_sha256":"b8fd409fa337e240945a9d17728f49b40c761d6d053ef2db06583189fbe4c2a0"},"schema_version":"1.0"},"canonical_sha256":"351590dbf694e79a11c7320e1a7a65ecd706ba19fc483dcc9cd9d479f21176a1","source":{"kind":"arxiv","id":"2303.06147","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.06147","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"arxiv_version","alias_value":"2303.06147v2","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.06147","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"pith_short_12","alias_value":"GUKZBW7WSTTZ","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"pith_short_16","alias_value":"GUKZBW7WSTTZUEOH","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"pith_short_8","alias_value":"GUKZBW7W","created_at":"2026-07-05T06:33:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GUKZBW7WSTTZUEOHGIHBU6TF5T","target":"record","payload":{"canonical_record":{"source":{"id":"2303.06147","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-10T18:59:57Z","cross_cats_sorted":[],"title_canon_sha256":"3a161627a2b4430300589ce9ce38b5a876a0ff2db5b6bfdcb4da0a693abdb61a","abstract_canon_sha256":"b8fd409fa337e240945a9d17728f49b40c761d6d053ef2db06583189fbe4c2a0"},"schema_version":"1.0"},"canonical_sha256":"351590dbf694e79a11c7320e1a7a65ecd706ba19fc483dcc9cd9d479f21176a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:33:44.179643Z","signature_b64":"nuHqUI/IYSpehlEe21itgfKu08GvgMSSbSZ9NRfV4ChnREmxWJFvSduqtSpXT0IsdfEYqeMqv6WbT0QYp6kdBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"351590dbf694e79a11c7320e1a7a65ecd706ba19fc483dcc9cd9d479f21176a1","last_reissued_at":"2026-07-05T06:33:44.179203Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:33:44.179203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.06147","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":"2026-07-05T06:33:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6KeIXF+c+MTdcJDN2abh5FNgPSMd7BMWCGUolLGhVvKVGefMK60C3DLVV57nnGkc8azknYw3mXKYw9waJvr3BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T16:13:05.023823Z"},"content_sha256":"2130dee4a9357d54b2b88364016c93ecf7f758cd2e38f32beb16f95cae45f620","schema_version":"1.0","event_id":"sha256:2130dee4a9357d54b2b88364016c93ecf7f758cd2e38f32beb16f95cae45f620"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GUKZBW7WSTTZUEOHGIHBU6TF5T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exphormer: Sparse Transformers for Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ali Kemal Sinop, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Hamed Shirzad","submitted_at":"2023-03-10T18:59:57Z","abstract_excerpt":"Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs while maintaining accuracy competitive with message-passing networks. In this paper, we introduce Exphormer, a framework for building powerful and scalable graph transformers. Exphormer consists of a sparse attention mechanism based on two mechanisms: virtual global nodes and expander graphs, whose mathematical characteristics, such as spectral expansion, pseduorandomness, and sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.06147","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/2303.06147/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":"2026-07-05T06:33:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qPjZdZm8yF60JzeUDLmMifYEtW73BlKeoMcr+8w8M0dTn5m1HCB3ONBqw/L0O0ZV/afLyOGySCusj0pk/NjjAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T16:13:05.024684Z"},"content_sha256":"a860606253f1cef52dec0b23aec005d541a5459558e72730fe619dacbe4a89f7","schema_version":"1.0","event_id":"sha256:a860606253f1cef52dec0b23aec005d541a5459558e72730fe619dacbe4a89f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GUKZBW7WSTTZUEOHGIHBU6TF5T/bundle.json","state_url":"https://pith.science/pith/GUKZBW7WSTTZUEOHGIHBU6TF5T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GUKZBW7WSTTZUEOHGIHBU6TF5T/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":"2026-08-17T16:13:05Z","links":{"resolver":"https://pith.science/pith/GUKZBW7WSTTZUEOHGIHBU6TF5T","bundle":"https://pith.science/pith/GUKZBW7WSTTZUEOHGIHBU6TF5T/bundle.json","state":"https://pith.science/pith/GUKZBW7WSTTZUEOHGIHBU6TF5T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GUKZBW7WSTTZUEOHGIHBU6TF5T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GUKZBW7WSTTZUEOHGIHBU6TF5T","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":"b8fd409fa337e240945a9d17728f49b40c761d6d053ef2db06583189fbe4c2a0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-10T18:59:57Z","title_canon_sha256":"3a161627a2b4430300589ce9ce38b5a876a0ff2db5b6bfdcb4da0a693abdb61a"},"schema_version":"1.0","source":{"id":"2303.06147","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.06147","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"arxiv_version","alias_value":"2303.06147v2","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.06147","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"pith_short_12","alias_value":"GUKZBW7WSTTZ","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"pith_short_16","alias_value":"GUKZBW7WSTTZUEOH","created_at":"2026-07-05T06:33:44Z"},{"alias_kind":"pith_short_8","alias_value":"GUKZBW7W","created_at":"2026-07-05T06:33:44Z"}],"graph_snapshots":[{"event_id":"sha256:a860606253f1cef52dec0b23aec005d541a5459558e72730fe619dacbe4a89f7","target":"graph","created_at":"2026-07-05T06:33:44Z","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/2303.06147/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs while maintaining accuracy competitive with message-passing networks. In this paper, we introduce Exphormer, a framework for building powerful and scalable graph transformers. Exphormer consists of a sparse attention mechanism based on two mechanisms: virtual global nodes and expander graphs, whose mathematical characteristics, such as spectral expansion, pseduorandomness, and sp","authors_text":"Ali Kemal Sinop, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Hamed Shirzad","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-10T18:59:57Z","title":"Exphormer: Sparse Transformers for Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.06147","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":"sha256:2130dee4a9357d54b2b88364016c93ecf7f758cd2e38f32beb16f95cae45f620","target":"record","created_at":"2026-07-05T06:33:44Z","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":"b8fd409fa337e240945a9d17728f49b40c761d6d053ef2db06583189fbe4c2a0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-10T18:59:57Z","title_canon_sha256":"3a161627a2b4430300589ce9ce38b5a876a0ff2db5b6bfdcb4da0a693abdb61a"},"schema_version":"1.0","source":{"id":"2303.06147","kind":"arxiv","version":2}},"canonical_sha256":"351590dbf694e79a11c7320e1a7a65ecd706ba19fc483dcc9cd9d479f21176a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"351590dbf694e79a11c7320e1a7a65ecd706ba19fc483dcc9cd9d479f21176a1","first_computed_at":"2026-07-05T06:33:44.179203Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:33:44.179203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nuHqUI/IYSpehlEe21itgfKu08GvgMSSbSZ9NRfV4ChnREmxWJFvSduqtSpXT0IsdfEYqeMqv6WbT0QYp6kdBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:33:44.179643Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.06147","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2130dee4a9357d54b2b88364016c93ecf7f758cd2e38f32beb16f95cae45f620","sha256:a860606253f1cef52dec0b23aec005d541a5459558e72730fe619dacbe4a89f7"],"state_sha256":"2e68716001d1c6e568f094776037570f24831048850a36ccf8b760b7fe90576e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D30DLra6h7+Lzk88zCjt75miX5sJV8Oha5sOgCrqLHpYOhbHplbgafBK8zzvh4htFv6SmUwu4szfxlH49CnQCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T16:13:05.029138Z","bundle_sha256":"771bee15b7ed59aa17988bc78385d7b29c86e9f807bc2fcb552cd6bad7e4dddf"}}