{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6MIUV2ZVFKBLTB75MUNNRYV2YD","short_pith_number":"pith:6MIUV2ZV","canonical_record":{"source":{"id":"1805.02396","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-05-07T08:28:45Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"8025d92351e0ded517e10104a4ae4fabfed07395bd54c7deda820f2f9cbb62e5","abstract_canon_sha256":"41d511a8378fcbbd8600ccc75a02fd7dc42057dda178cbaa3625c6b0a9b5e612"},"schema_version":"1.0"},"canonical_sha256":"f3114aeb352a82b987fd651ad8e2bac0d8371d5941d88b801be6c20aac826ba1","source":{"kind":"arxiv","id":"1805.02396","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.02396","created_at":"2026-05-18T00:06:11Z"},{"alias_kind":"arxiv_version","alias_value":"1805.02396v2","created_at":"2026-05-18T00:06:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.02396","created_at":"2026-05-18T00:06:11Z"},{"alias_kind":"pith_short_12","alias_value":"6MIUV2ZVFKBL","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6MIUV2ZVFKBLTB75","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6MIUV2ZV","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6MIUV2ZVFKBLTB75MUNNRYV2YD","target":"record","payload":{"canonical_record":{"source":{"id":"1805.02396","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-05-07T08:28:45Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"8025d92351e0ded517e10104a4ae4fabfed07395bd54c7deda820f2f9cbb62e5","abstract_canon_sha256":"41d511a8378fcbbd8600ccc75a02fd7dc42057dda178cbaa3625c6b0a9b5e612"},"schema_version":"1.0"},"canonical_sha256":"f3114aeb352a82b987fd651ad8e2bac0d8371d5941d88b801be6c20aac826ba1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:11.515885Z","signature_b64":"oMw5OsN2Zhg1q+3J0UdrSqgZ/9awHJKde6aOvPMZdBGvqvdaRHIykJthl5LZMEOCHfVLMcoKKnofCjeVZgFQCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3114aeb352a82b987fd651ad8e2bac0d8371d5941d88b801be6c20aac826ba1","last_reissued_at":"2026-05-18T00:06:11.515141Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:11.515141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.02396","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":"2026-05-18T00:06:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uTIjNfYRYRV6bKhN8tPnFZEcmY87Yf2ljqNlKImQgLnT3B+a6/GT15wl5KMlSA3oIzsdux1nPhOBT/C6YN58Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T08:01:49.287308Z"},"content_sha256":"fc891875ab539356c879afaad23efa20df76c0a11b35cefc9386865d9c6f5fa6","schema_version":"1.0","event_id":"sha256:fc891875ab539356c879afaad23efa20df76c0a11b35cefc9386865d9c6f5fa6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6MIUV2ZVFKBLTB75MUNNRYV2YD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Billion-scale Network Embedding with Iterative Random Projection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.SI","authors_text":"Haoyang Li, Peng Cui, Wenwu Zhu, Xiao Wang, Ziwei Zhang","submitted_at":"2018-05-07T08:28:45Z","abstract_excerpt":"Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently. However, the existing methods are incapable of handling billion-scale networks, because they are computationally expensive and, at the same time, difficult to be accelerated by distributed computing schemes. To address these problems, we propose RandNE (Iterative Random Projection Network Embedding), a novel and simple billion-scale network embedding method. Specifically, we propose a Gaussian random projection approach to map the network into "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.02396","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":""},"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":"2026-05-18T00:06:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dCbAOLWn1dDQvl3PylGyO+llEbuiDz08AvRsLudiQavXJMUJcCXO5VW2hM+98sISu+owt8OC0UF6Sajys5/5DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T08:01:49.288012Z"},"content_sha256":"9ceed4221addcf2efbd2d6edf5f7b8bb67d39b66c7e8e4a0b9e515413683b998","schema_version":"1.0","event_id":"sha256:9ceed4221addcf2efbd2d6edf5f7b8bb67d39b66c7e8e4a0b9e515413683b998"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6MIUV2ZVFKBLTB75MUNNRYV2YD/bundle.json","state_url":"https://pith.science/pith/6MIUV2ZVFKBLTB75MUNNRYV2YD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6MIUV2ZVFKBLTB75MUNNRYV2YD/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":"2026-05-31T08:01:49Z","links":{"resolver":"https://pith.science/pith/6MIUV2ZVFKBLTB75MUNNRYV2YD","bundle":"https://pith.science/pith/6MIUV2ZVFKBLTB75MUNNRYV2YD/bundle.json","state":"https://pith.science/pith/6MIUV2ZVFKBLTB75MUNNRYV2YD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6MIUV2ZVFKBLTB75MUNNRYV2YD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6MIUV2ZVFKBLTB75MUNNRYV2YD","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":"41d511a8378fcbbd8600ccc75a02fd7dc42057dda178cbaa3625c6b0a9b5e612","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-05-07T08:28:45Z","title_canon_sha256":"8025d92351e0ded517e10104a4ae4fabfed07395bd54c7deda820f2f9cbb62e5"},"schema_version":"1.0","source":{"id":"1805.02396","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.02396","created_at":"2026-05-18T00:06:11Z"},{"alias_kind":"arxiv_version","alias_value":"1805.02396v2","created_at":"2026-05-18T00:06:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.02396","created_at":"2026-05-18T00:06:11Z"},{"alias_kind":"pith_short_12","alias_value":"6MIUV2ZVFKBL","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6MIUV2ZVFKBLTB75","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6MIUV2ZV","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:9ceed4221addcf2efbd2d6edf5f7b8bb67d39b66c7e8e4a0b9e515413683b998","target":"graph","created_at":"2026-05-18T00:06:11Z","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":"Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently. However, the existing methods are incapable of handling billion-scale networks, because they are computationally expensive and, at the same time, difficult to be accelerated by distributed computing schemes. To address these problems, we propose RandNE (Iterative Random Projection Network Embedding), a novel and simple billion-scale network embedding method. Specifically, we propose a Gaussian random projection approach to map the network into ","authors_text":"Haoyang Li, Peng Cui, Wenwu Zhu, Xiao Wang, Ziwei Zhang","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-05-07T08:28:45Z","title":"Billion-scale Network Embedding with Iterative Random Projection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.02396","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:fc891875ab539356c879afaad23efa20df76c0a11b35cefc9386865d9c6f5fa6","target":"record","created_at":"2026-05-18T00:06:11Z","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":"41d511a8378fcbbd8600ccc75a02fd7dc42057dda178cbaa3625c6b0a9b5e612","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-05-07T08:28:45Z","title_canon_sha256":"8025d92351e0ded517e10104a4ae4fabfed07395bd54c7deda820f2f9cbb62e5"},"schema_version":"1.0","source":{"id":"1805.02396","kind":"arxiv","version":2}},"canonical_sha256":"f3114aeb352a82b987fd651ad8e2bac0d8371d5941d88b801be6c20aac826ba1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f3114aeb352a82b987fd651ad8e2bac0d8371d5941d88b801be6c20aac826ba1","first_computed_at":"2026-05-18T00:06:11.515141Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:11.515141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oMw5OsN2Zhg1q+3J0UdrSqgZ/9awHJKde6aOvPMZdBGvqvdaRHIykJthl5LZMEOCHfVLMcoKKnofCjeVZgFQCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:11.515885Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.02396","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc891875ab539356c879afaad23efa20df76c0a11b35cefc9386865d9c6f5fa6","sha256:9ceed4221addcf2efbd2d6edf5f7b8bb67d39b66c7e8e4a0b9e515413683b998"],"state_sha256":"3c37084acfa4f070cc96a0b89f126e8e121cd8546084dd9a58459498b556000d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c6uPGuOH9sUmch1fRhzFYhSWzIVCC2engwdHGoykgJN0/Jh5/xMUfYlCEMzbjPZeZFLpd65XD14U/Qt2pVW/Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T08:01:49.292063Z","bundle_sha256":"0dc3aa98c22533ee48518435ec860025bd1673b4929beb549ad854a7030ac610"}}