{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:V5UIO625FXB7FU2U3VK2YXI7FZ","short_pith_number":"pith:V5UIO625","canonical_record":{"source":{"id":"1811.06237","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-11-15T08:50:34Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f48d67bce41e29850cf0712e2c6e4b05494154d6dacc7a5e6c2ba915e2691661","abstract_canon_sha256":"4dc0025e64f86a4ba4703e7e8e41a3cb446674c041d2d2371222162f3e3b5ee5"},"schema_version":"1.0"},"canonical_sha256":"af68877b5d2dc3f2d354dd55ac5d1f2e5d299f69a8bbf9b9338874b3d06eb810","source":{"kind":"arxiv","id":"1811.06237","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.06237","created_at":"2026-05-18T00:00:38Z"},{"alias_kind":"arxiv_version","alias_value":"1811.06237v1","created_at":"2026-05-18T00:00:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06237","created_at":"2026-05-18T00:00:38Z"},{"alias_kind":"pith_short_12","alias_value":"V5UIO625FXB7","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"V5UIO625FXB7FU2U","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"V5UIO625","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:V5UIO625FXB7FU2U3VK2YXI7FZ","target":"record","payload":{"canonical_record":{"source":{"id":"1811.06237","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-11-15T08:50:34Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f48d67bce41e29850cf0712e2c6e4b05494154d6dacc7a5e6c2ba915e2691661","abstract_canon_sha256":"4dc0025e64f86a4ba4703e7e8e41a3cb446674c041d2d2371222162f3e3b5ee5"},"schema_version":"1.0"},"canonical_sha256":"af68877b5d2dc3f2d354dd55ac5d1f2e5d299f69a8bbf9b9338874b3d06eb810","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:38.265608Z","signature_b64":"aykrlvXRb7I2ejc/7fB7sJ78ktk6ib78zIIEXJGQ1Gzy9MsFBDpDjNTmPpE6UWzgPQxa/GA3D6u5YNDJ1rDXCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af68877b5d2dc3f2d354dd55ac5d1f2e5d299f69a8bbf9b9338874b3d06eb810","last_reissued_at":"2026-05-18T00:00:38.265111Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:38.265111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.06237","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":"2026-05-18T00:00:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5K2pt4N1l0eGiUMWNYtjOHYQuDc5u+5ZmMl95ezGXJoJaIBQzvooLD1GuV4gmMMxaOCA4DkZtDrKn27qnndODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T20:03:19.774988Z"},"content_sha256":"2881fddcc20422894dd29a29ca949c713e7051e97d9c3af965160763bb605c64","schema_version":"1.0","event_id":"sha256:2881fddcc20422894dd29a29ca949c713e7051e97d9c3af965160763bb605c64"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:V5UIO625FXB7FU2U3VK2YXI7FZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SGR: Self-Supervised Spectral Graph Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SI","authors_text":"Alex Bronstein, Anton Tsitsulin, Davide Mottin, Emmanuel M\\\"uller, Panagiotis Karras","submitted_at":"2018-11-15T08:50:34Z","abstract_excerpt":"Representing a graph as a vector is a challenging task; ideally, the representation should be easily computable and conducive to efficient comparisons among graphs, tailored to the particular data and analytical task at hand. Unfortunately, a \"one-size-fits-all\" solution is unattainable, as different analytical tasks may require different attention to global or local graph features. We develop SGR, the first, to our knowledge, method for learning graph representations in a self-supervised manner. Grounded on spectral graph analysis, SGR seamlessly combines all aforementioned desirable properti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06237","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":""},"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":"2026-05-18T00:00:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ttzLF72KYBFVevz9TpJRwq1D89S2LN0rC2ZAWhd7s6ZQZMfaKK+cALS4fZalxxo4HmhaDgLGxs8cc0TFhueYAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T20:03:19.775353Z"},"content_sha256":"b4a6e78b12a87c6142451245c7b88a38cb1f3e7e7f38f23fb2616a685990d320","schema_version":"1.0","event_id":"sha256:b4a6e78b12a87c6142451245c7b88a38cb1f3e7e7f38f23fb2616a685990d320"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V5UIO625FXB7FU2U3VK2YXI7FZ/bundle.json","state_url":"https://pith.science/pith/V5UIO625FXB7FU2U3VK2YXI7FZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V5UIO625FXB7FU2U3VK2YXI7FZ/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":"2026-06-24T20:03:19Z","links":{"resolver":"https://pith.science/pith/V5UIO625FXB7FU2U3VK2YXI7FZ","bundle":"https://pith.science/pith/V5UIO625FXB7FU2U3VK2YXI7FZ/bundle.json","state":"https://pith.science/pith/V5UIO625FXB7FU2U3VK2YXI7FZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V5UIO625FXB7FU2U3VK2YXI7FZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:V5UIO625FXB7FU2U3VK2YXI7FZ","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":"4dc0025e64f86a4ba4703e7e8e41a3cb446674c041d2d2371222162f3e3b5ee5","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-11-15T08:50:34Z","title_canon_sha256":"f48d67bce41e29850cf0712e2c6e4b05494154d6dacc7a5e6c2ba915e2691661"},"schema_version":"1.0","source":{"id":"1811.06237","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.06237","created_at":"2026-05-18T00:00:38Z"},{"alias_kind":"arxiv_version","alias_value":"1811.06237v1","created_at":"2026-05-18T00:00:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06237","created_at":"2026-05-18T00:00:38Z"},{"alias_kind":"pith_short_12","alias_value":"V5UIO625FXB7","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"V5UIO625FXB7FU2U","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"V5UIO625","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:b4a6e78b12a87c6142451245c7b88a38cb1f3e7e7f38f23fb2616a685990d320","target":"graph","created_at":"2026-05-18T00:00:38Z","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"},"paper":{"abstract_excerpt":"Representing a graph as a vector is a challenging task; ideally, the representation should be easily computable and conducive to efficient comparisons among graphs, tailored to the particular data and analytical task at hand. Unfortunately, a \"one-size-fits-all\" solution is unattainable, as different analytical tasks may require different attention to global or local graph features. We develop SGR, the first, to our knowledge, method for learning graph representations in a self-supervised manner. Grounded on spectral graph analysis, SGR seamlessly combines all aforementioned desirable properti","authors_text":"Alex Bronstein, Anton Tsitsulin, Davide Mottin, Emmanuel M\\\"uller, Panagiotis Karras","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-11-15T08:50:34Z","title":"SGR: Self-Supervised Spectral Graph Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06237","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":"sha256:2881fddcc20422894dd29a29ca949c713e7051e97d9c3af965160763bb605c64","target":"record","created_at":"2026-05-18T00:00:38Z","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":"4dc0025e64f86a4ba4703e7e8e41a3cb446674c041d2d2371222162f3e3b5ee5","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-11-15T08:50:34Z","title_canon_sha256":"f48d67bce41e29850cf0712e2c6e4b05494154d6dacc7a5e6c2ba915e2691661"},"schema_version":"1.0","source":{"id":"1811.06237","kind":"arxiv","version":1}},"canonical_sha256":"af68877b5d2dc3f2d354dd55ac5d1f2e5d299f69a8bbf9b9338874b3d06eb810","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"af68877b5d2dc3f2d354dd55ac5d1f2e5d299f69a8bbf9b9338874b3d06eb810","first_computed_at":"2026-05-18T00:00:38.265111Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:38.265111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aykrlvXRb7I2ejc/7fB7sJ78ktk6ib78zIIEXJGQ1Gzy9MsFBDpDjNTmPpE6UWzgPQxa/GA3D6u5YNDJ1rDXCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:38.265608Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.06237","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2881fddcc20422894dd29a29ca949c713e7051e97d9c3af965160763bb605c64","sha256:b4a6e78b12a87c6142451245c7b88a38cb1f3e7e7f38f23fb2616a685990d320"],"state_sha256":"b79c7e64d951ba1c711427fddce3ae5d2064a41da7d72e5f060f9a4bd4637c79"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z2I83I4vVqVzUqSY0ZHnJWFuDB/EUY2X59pfdkep5v2VlYBpkGX6majiJaRZYFnG37eUUBCM74oUVG0TS/8KDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T20:03:19.777302Z","bundle_sha256":"a0e9a7ac2053c448bf648b78a37da17fb9170877b3be944ea0b1e0ed65dc1f59"}}