{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:ERMYHSVED5BNJCBNINHHD5DRCR","short_pith_number":"pith:ERMYHSVE","canonical_record":{"source":{"id":"1503.03578","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.LG","submitted_at":"2015-03-12T04:07:32Z","cross_cats_sorted":[],"title_canon_sha256":"d2cd3d30adb8af9ebd28d46a11dc79a80e4158ee1b9e46dd69a9f43c8137c6b7","abstract_canon_sha256":"569931fecbde30cac2d53e719bfebad5c309619a179c7d8cd72d1e4b07329e16"},"schema_version":"1.0"},"canonical_sha256":"245983caa41f42d4882d434e71f47114446e1ccff7b77b857f3b693e1a33ed54","source":{"kind":"arxiv","id":"1503.03578","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.03578","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"arxiv_version","alias_value":"1503.03578v1","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.03578","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"pith_short_12","alias_value":"ERMYHSVED5BN","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"ERMYHSVED5BNJCBN","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"ERMYHSVE","created_at":"2026-05-18T12:29:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:ERMYHSVED5BNJCBNINHHD5DRCR","target":"record","payload":{"canonical_record":{"source":{"id":"1503.03578","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.LG","submitted_at":"2015-03-12T04:07:32Z","cross_cats_sorted":[],"title_canon_sha256":"d2cd3d30adb8af9ebd28d46a11dc79a80e4158ee1b9e46dd69a9f43c8137c6b7","abstract_canon_sha256":"569931fecbde30cac2d53e719bfebad5c309619a179c7d8cd72d1e4b07329e16"},"schema_version":"1.0"},"canonical_sha256":"245983caa41f42d4882d434e71f47114446e1ccff7b77b857f3b693e1a33ed54","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:24:58.107456Z","signature_b64":"eKTevtLoxVTJNYzhgrXUn/eQl4AJGFUNlca5lMF4nm1pFM7K9mmZAY2Uo/SsOyRDLflqZ5KAnwWBI4YdjxM3DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"245983caa41f42d4882d434e71f47114446e1ccff7b77b857f3b693e1a33ed54","last_reissued_at":"2026-05-18T02:24:58.106756Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:24:58.106756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1503.03578","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":"2026-05-18T02:24:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/F8lhd5O4aoXu562TEiD5/WZjPL3VjiKtxV6PGTe2CngXLfVMMaVxh3qamH7KKCY7SX4i/Ahf0CvJ0fWF8HWAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T11:46:33.105646Z"},"content_sha256":"1543e4f7bc22898c24859ed3c3ab4efa03e781bf1f0f8648b091a4e67ce16748","schema_version":"1.0","event_id":"sha256:1543e4f7bc22898c24859ed3c3ab4efa03e781bf1f0f8648b091a4e67ce16748"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:ERMYHSVED5BNJCBNINHHD5DRCR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LINE: Large-scale Information Network Embedding","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jian Tang, Jun Yan, Meng Qu, Ming Zhang, Mingzhe Wang, Qiaozhu Mei","submitted_at":"2015-03-12T04:07:32Z","abstract_excerpt":"This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the \"LINE,\" which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.03578","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":"2026-05-18T02:24:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gokJZEtzl+MH9/hPJO1PTOJDJU4Yd7PHFc4StombkSDiQ8gHCcE37ffEM/CXUwX97cLxnG8SiCWmvrwmTHbBBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T11:46:33.105997Z"},"content_sha256":"b623fd3b77802e17b8f7810456ce66054b7d2faf59bdd7b969126f45d6478b27","schema_version":"1.0","event_id":"sha256:b623fd3b77802e17b8f7810456ce66054b7d2faf59bdd7b969126f45d6478b27"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ERMYHSVED5BNJCBNINHHD5DRCR/bundle.json","state_url":"https://pith.science/pith/ERMYHSVED5BNJCBNINHHD5DRCR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ERMYHSVED5BNJCBNINHHD5DRCR/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":"2026-05-21T11:46:33Z","links":{"resolver":"https://pith.science/pith/ERMYHSVED5BNJCBNINHHD5DRCR","bundle":"https://pith.science/pith/ERMYHSVED5BNJCBNINHHD5DRCR/bundle.json","state":"https://pith.science/pith/ERMYHSVED5BNJCBNINHHD5DRCR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ERMYHSVED5BNJCBNINHHD5DRCR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:ERMYHSVED5BNJCBNINHHD5DRCR","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":"569931fecbde30cac2d53e719bfebad5c309619a179c7d8cd72d1e4b07329e16","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.LG","submitted_at":"2015-03-12T04:07:32Z","title_canon_sha256":"d2cd3d30adb8af9ebd28d46a11dc79a80e4158ee1b9e46dd69a9f43c8137c6b7"},"schema_version":"1.0","source":{"id":"1503.03578","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.03578","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"arxiv_version","alias_value":"1503.03578v1","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.03578","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"pith_short_12","alias_value":"ERMYHSVED5BN","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"ERMYHSVED5BNJCBN","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"ERMYHSVE","created_at":"2026-05-18T12:29:19Z"}],"graph_snapshots":[{"event_id":"sha256:b623fd3b77802e17b8f7810456ce66054b7d2faf59bdd7b969126f45d6478b27","target":"graph","created_at":"2026-05-18T02:24:58Z","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":"This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the \"LINE,\" which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the ","authors_text":"Jian Tang, Jun Yan, Meng Qu, Ming Zhang, Mingzhe Wang, Qiaozhu Mei","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.LG","submitted_at":"2015-03-12T04:07:32Z","title":"LINE: Large-scale Information Network Embedding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.03578","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":"sha256:1543e4f7bc22898c24859ed3c3ab4efa03e781bf1f0f8648b091a4e67ce16748","target":"record","created_at":"2026-05-18T02:24:58Z","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":"569931fecbde30cac2d53e719bfebad5c309619a179c7d8cd72d1e4b07329e16","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.LG","submitted_at":"2015-03-12T04:07:32Z","title_canon_sha256":"d2cd3d30adb8af9ebd28d46a11dc79a80e4158ee1b9e46dd69a9f43c8137c6b7"},"schema_version":"1.0","source":{"id":"1503.03578","kind":"arxiv","version":1}},"canonical_sha256":"245983caa41f42d4882d434e71f47114446e1ccff7b77b857f3b693e1a33ed54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"245983caa41f42d4882d434e71f47114446e1ccff7b77b857f3b693e1a33ed54","first_computed_at":"2026-05-18T02:24:58.106756Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:24:58.106756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eKTevtLoxVTJNYzhgrXUn/eQl4AJGFUNlca5lMF4nm1pFM7K9mmZAY2Uo/SsOyRDLflqZ5KAnwWBI4YdjxM3DQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:24:58.107456Z","signed_message":"canonical_sha256_bytes"},"source_id":"1503.03578","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1543e4f7bc22898c24859ed3c3ab4efa03e781bf1f0f8648b091a4e67ce16748","sha256:b623fd3b77802e17b8f7810456ce66054b7d2faf59bdd7b969126f45d6478b27"],"state_sha256":"9bbc4f907671a50d8779dce1002e4dcb2086ddb47818c02a4032b5b47b5c8147"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NKqwUfrbPhhJxw/b77A4BN+IAqFXT+ClrH29Vk+BKi/vJKplX3jorUWkHfrN2wqqRleakaDXpmo5dSQUhu52Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T11:46:33.107981Z","bundle_sha256":"7a77f069cae844f2992cd12fe2cb4e21584f847bfe6c8b85b5b6b08ba749f0ca"}}