{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:HAH5YSVMEMTRMN6PA2GOTURKT6","short_pith_number":"pith:HAH5YSVM","canonical_record":{"source":{"id":"1710.02836","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-08T14:23:30Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"cdeb56ac9f75c3832be8c78787013e85a47b3f0f78cea9e21f650835129d9241","abstract_canon_sha256":"30c16bf67967e9925b97cc3e8d05a8c32e5ce297b46cad39469dd3d47eaa1f7b"},"schema_version":"1.0"},"canonical_sha256":"380fdc4aac23271637cf068ce9d22a9f893a14b6cffda37d9972216cc7bf2ed4","source":{"kind":"arxiv","id":"1710.02836","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.02836","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"arxiv_version","alias_value":"1710.02836v1","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.02836","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"pith_short_12","alias_value":"HAH5YSVMEMTR","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"HAH5YSVMEMTRMN6P","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"HAH5YSVM","created_at":"2026-05-18T12:31:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:HAH5YSVMEMTRMN6PA2GOTURKT6","target":"record","payload":{"canonical_record":{"source":{"id":"1710.02836","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-08T14:23:30Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"cdeb56ac9f75c3832be8c78787013e85a47b3f0f78cea9e21f650835129d9241","abstract_canon_sha256":"30c16bf67967e9925b97cc3e8d05a8c32e5ce297b46cad39469dd3d47eaa1f7b"},"schema_version":"1.0"},"canonical_sha256":"380fdc4aac23271637cf068ce9d22a9f893a14b6cffda37d9972216cc7bf2ed4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:28.719567Z","signature_b64":"HmaVxBIyg3oXmJ44+G4x0XtB9UX1wH5rUUe8oWvCQz+GSIosSG6xb6i7XBS2xzxGDrQ3nVXiR7cM8Tqax+H2Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"380fdc4aac23271637cf068ce9d22a9f893a14b6cffda37d9972216cc7bf2ed4","last_reissued_at":"2026-05-18T00:33:28.719108Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:28.719108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.02836","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":"2026-05-18T00:33:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wvuro8Oh1lXv84ZohvaEZIKbDNpT+aC9sBD+glb+DkaMTEhjO82yjrf7A6nvEB+LLHw/JRM0yvzFWsu33aDmCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T12:03:55.932275Z"},"content_sha256":"e1c3031c0550938bdeab2ecf370f2df17f4a6590e1eb4d7b88baf3ccb4ccbbcb","schema_version":"1.0","event_id":"sha256:e1c3031c0550938bdeab2ecf370f2df17f4a6590e1eb4d7b88baf3ccb4ccbbcb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:HAH5YSVMEMTRMN6PA2GOTURKT6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RUM: network Representation learning throUgh Multi-level structural information preservation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.LG","authors_text":"Guoping Zhao, Jiajun Liu, Ji-Rong Wen, Kai Zheng, Yanlei Yu, Zhiwu Lu","submitted_at":"2017-10-08T14:23:30Z","abstract_excerpt":"We have witnessed the discovery of many techniques for network representation learning in recent years, ranging from encoding the context in random walks to embedding the lower order connections, to finding latent space representations with auto-encoders. However, existing techniques are looking mostly into the local structures in a network, while higher-level properties such as global community structures are often neglected. We propose a novel network representations learning model framework called RUM (network Representation learning throUgh Multi-level structural information preservation)."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.02836","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":"2026-05-18T00:33:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rzxZly06SStg4hD2NZobJCffu5bHdUgM/NO/s7ZRbHNurYeHC7Imh+bAgIugG2+8NRqL+Bm+O4kxrJmNGkVaCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T12:03:55.932981Z"},"content_sha256":"08eb1da4aadf7c95e6eac56b70387a9afde174c25a7b6791ad2d633030973120","schema_version":"1.0","event_id":"sha256:08eb1da4aadf7c95e6eac56b70387a9afde174c25a7b6791ad2d633030973120"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HAH5YSVMEMTRMN6PA2GOTURKT6/bundle.json","state_url":"https://pith.science/pith/HAH5YSVMEMTRMN6PA2GOTURKT6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HAH5YSVMEMTRMN6PA2GOTURKT6/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":"2026-05-25T12:03:55Z","links":{"resolver":"https://pith.science/pith/HAH5YSVMEMTRMN6PA2GOTURKT6","bundle":"https://pith.science/pith/HAH5YSVMEMTRMN6PA2GOTURKT6/bundle.json","state":"https://pith.science/pith/HAH5YSVMEMTRMN6PA2GOTURKT6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HAH5YSVMEMTRMN6PA2GOTURKT6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:HAH5YSVMEMTRMN6PA2GOTURKT6","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":"30c16bf67967e9925b97cc3e8d05a8c32e5ce297b46cad39469dd3d47eaa1f7b","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-08T14:23:30Z","title_canon_sha256":"cdeb56ac9f75c3832be8c78787013e85a47b3f0f78cea9e21f650835129d9241"},"schema_version":"1.0","source":{"id":"1710.02836","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.02836","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"arxiv_version","alias_value":"1710.02836v1","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.02836","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"pith_short_12","alias_value":"HAH5YSVMEMTR","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"HAH5YSVMEMTRMN6P","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"HAH5YSVM","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:08eb1da4aadf7c95e6eac56b70387a9afde174c25a7b6791ad2d633030973120","target":"graph","created_at":"2026-05-18T00:33:28Z","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":"We have witnessed the discovery of many techniques for network representation learning in recent years, ranging from encoding the context in random walks to embedding the lower order connections, to finding latent space representations with auto-encoders. However, existing techniques are looking mostly into the local structures in a network, while higher-level properties such as global community structures are often neglected. We propose a novel network representations learning model framework called RUM (network Representation learning throUgh Multi-level structural information preservation).","authors_text":"Guoping Zhao, Jiajun Liu, Ji-Rong Wen, Kai Zheng, Yanlei Yu, Zhiwu Lu","cross_cats":["cs.SI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-08T14:23:30Z","title":"RUM: network Representation learning throUgh Multi-level structural information preservation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.02836","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:e1c3031c0550938bdeab2ecf370f2df17f4a6590e1eb4d7b88baf3ccb4ccbbcb","target":"record","created_at":"2026-05-18T00:33:28Z","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":"30c16bf67967e9925b97cc3e8d05a8c32e5ce297b46cad39469dd3d47eaa1f7b","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-08T14:23:30Z","title_canon_sha256":"cdeb56ac9f75c3832be8c78787013e85a47b3f0f78cea9e21f650835129d9241"},"schema_version":"1.0","source":{"id":"1710.02836","kind":"arxiv","version":1}},"canonical_sha256":"380fdc4aac23271637cf068ce9d22a9f893a14b6cffda37d9972216cc7bf2ed4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"380fdc4aac23271637cf068ce9d22a9f893a14b6cffda37d9972216cc7bf2ed4","first_computed_at":"2026-05-18T00:33:28.719108Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:33:28.719108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HmaVxBIyg3oXmJ44+G4x0XtB9UX1wH5rUUe8oWvCQz+GSIosSG6xb6i7XBS2xzxGDrQ3nVXiR7cM8Tqax+H2Dw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:33:28.719567Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.02836","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e1c3031c0550938bdeab2ecf370f2df17f4a6590e1eb4d7b88baf3ccb4ccbbcb","sha256:08eb1da4aadf7c95e6eac56b70387a9afde174c25a7b6791ad2d633030973120"],"state_sha256":"df119e3d9aa5b7b4938d78af8bc159aea7abf78e72c7b0c62376239183b98205"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LZpGhDUV25+toKwXwPSWRg9JffYXJmxoEjxH1gF9XWRpJ/2gXd0njPDZgoBCyKeTw3i503x3xGXmJdRE/rLADA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T12:03:55.936693Z","bundle_sha256":"c9d582aff795c48c93f44c12389670207fea318bb524e6932a2577dee390490a"}}