{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:K4S4DTCXGDTQZF3WSEULP5QJMC","short_pith_number":"pith:K4S4DTCX","canonical_record":{"source":{"id":"1910.02370","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-06T04:43:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7551b7b0bd4dfd52d28cc66ae7a66a9a764b53630f06ec266b48905d8ab5f1b6","abstract_canon_sha256":"6c2a0f71e5db91424b7646830de2dff3e0d8e7cf67d33b98d0397b9621241817"},"schema_version":"1.0"},"canonical_sha256":"5725c1cc5730e70c97769128b7f60960955cf989ea56745484d8c75378abbd1e","source":{"kind":"arxiv","id":"1910.02370","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02370","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02370v2","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02370","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"pith_short_12","alias_value":"K4S4DTCXGDTQ","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"pith_short_16","alias_value":"K4S4DTCXGDTQZF3W","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"pith_short_8","alias_value":"K4S4DTCX","created_at":"2026-07-05T02:50:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:K4S4DTCXGDTQZF3WSEULP5QJMC","target":"record","payload":{"canonical_record":{"source":{"id":"1910.02370","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-06T04:43:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7551b7b0bd4dfd52d28cc66ae7a66a9a764b53630f06ec266b48905d8ab5f1b6","abstract_canon_sha256":"6c2a0f71e5db91424b7646830de2dff3e0d8e7cf67d33b98d0397b9621241817"},"schema_version":"1.0"},"canonical_sha256":"5725c1cc5730e70c97769128b7f60960955cf989ea56745484d8c75378abbd1e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:50:37.415577Z","signature_b64":"8TmFxu9qkmBQ4sT2sIyQl8veJWobAWK1i0WGimCEP7W9A6JDHHuvsdkesdHMgkkBg9ROrC4OU23+O7nVhG7FDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5725c1cc5730e70c97769128b7f60960955cf989ea56745484d8c75378abbd1e","last_reissued_at":"2026-07-05T02:50:37.415160Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:50:37.415160Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.02370","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":"2026-07-05T02:50:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/npyS5GxzHP3tHUEi9rRImxm5FCJtLw/w1wLZdIIFPvuShRDV+mWq7rtI+jIRaGaNwDdFWWJ0JWR02fAzsEKDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:47:11.617429Z"},"content_sha256":"d84054fb0bf1034ae8821137e08774ad67769445764f96dd9e936f87de11b9e0","schema_version":"1.0","event_id":"sha256:d84054fb0bf1034ae8821137e08774ad67769445764f96dd9e936f87de11b9e0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:K4S4DTCXGDTQZF3WSEULP5QJMC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chenhui Deng, Yongyu Wang, Zhiqiang Zhao, Zhiru Zhang, Zhuo Feng","submitted_at":"2019-10-06T04:43:46Z","abstract_excerpt":"Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy. Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage. In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms. GraphZoom first p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02370","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/1910.02370/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":"2026-07-05T02:50:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6ZNNPvT7Dy6LBx/SJfkybuqN63njYd0UxTlNq9S4TPDwEriXs+tBvQpTM2jJDmJRdpJqi25gJ4+V1yABkg8lDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:47:11.617815Z"},"content_sha256":"da28b9943e5c25abac3a43307a91b714180cf879a4b183ee8d31beaef6c80aee","schema_version":"1.0","event_id":"sha256:da28b9943e5c25abac3a43307a91b714180cf879a4b183ee8d31beaef6c80aee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K4S4DTCXGDTQZF3WSEULP5QJMC/bundle.json","state_url":"https://pith.science/pith/K4S4DTCXGDTQZF3WSEULP5QJMC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K4S4DTCXGDTQZF3WSEULP5QJMC/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":"2026-07-06T23:47:11Z","links":{"resolver":"https://pith.science/pith/K4S4DTCXGDTQZF3WSEULP5QJMC","bundle":"https://pith.science/pith/K4S4DTCXGDTQZF3WSEULP5QJMC/bundle.json","state":"https://pith.science/pith/K4S4DTCXGDTQZF3WSEULP5QJMC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K4S4DTCXGDTQZF3WSEULP5QJMC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:K4S4DTCXGDTQZF3WSEULP5QJMC","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":"6c2a0f71e5db91424b7646830de2dff3e0d8e7cf67d33b98d0397b9621241817","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-06T04:43:46Z","title_canon_sha256":"7551b7b0bd4dfd52d28cc66ae7a66a9a764b53630f06ec266b48905d8ab5f1b6"},"schema_version":"1.0","source":{"id":"1910.02370","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02370","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02370v2","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02370","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"pith_short_12","alias_value":"K4S4DTCXGDTQ","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"pith_short_16","alias_value":"K4S4DTCXGDTQZF3W","created_at":"2026-07-05T02:50:37Z"},{"alias_kind":"pith_short_8","alias_value":"K4S4DTCX","created_at":"2026-07-05T02:50:37Z"}],"graph_snapshots":[{"event_id":"sha256:da28b9943e5c25abac3a43307a91b714180cf879a4b183ee8d31beaef6c80aee","target":"graph","created_at":"2026-07-05T02:50:37Z","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/1910.02370/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy. Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage. In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms. GraphZoom first p","authors_text":"Chenhui Deng, Yongyu Wang, Zhiqiang Zhao, Zhiru Zhang, Zhuo Feng","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-06T04:43:46Z","title":"GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02370","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:d84054fb0bf1034ae8821137e08774ad67769445764f96dd9e936f87de11b9e0","target":"record","created_at":"2026-07-05T02:50:37Z","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":"6c2a0f71e5db91424b7646830de2dff3e0d8e7cf67d33b98d0397b9621241817","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-06T04:43:46Z","title_canon_sha256":"7551b7b0bd4dfd52d28cc66ae7a66a9a764b53630f06ec266b48905d8ab5f1b6"},"schema_version":"1.0","source":{"id":"1910.02370","kind":"arxiv","version":2}},"canonical_sha256":"5725c1cc5730e70c97769128b7f60960955cf989ea56745484d8c75378abbd1e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5725c1cc5730e70c97769128b7f60960955cf989ea56745484d8c75378abbd1e","first_computed_at":"2026-07-05T02:50:37.415160Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:50:37.415160Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8TmFxu9qkmBQ4sT2sIyQl8veJWobAWK1i0WGimCEP7W9A6JDHHuvsdkesdHMgkkBg9ROrC4OU23+O7nVhG7FDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:50:37.415577Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.02370","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d84054fb0bf1034ae8821137e08774ad67769445764f96dd9e936f87de11b9e0","sha256:da28b9943e5c25abac3a43307a91b714180cf879a4b183ee8d31beaef6c80aee"],"state_sha256":"b4ff92b456e7f76769a073e5886e26e12f4e7ad6e2267a98306dd2a555fdb02b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pWo4bGQn5+V1LCi/d0quj1O+OUpGDm/j7tTtCvLn2JKjh2EUXuuDt3riajQ7X6NklIGfyo/PrwmJV9Top51jCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T23:47:11.620133Z","bundle_sha256":"9dcfffc28ed102f5a95bc2027f184efbd47b010950cf72397ce6d3c12984d41d"}}