{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6MS4KQAGM2JXPH52DFQMDBUE3M","short_pith_number":"pith:6MS4KQAG","canonical_record":{"source":{"id":"1709.06636","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2017-09-19T20:30:30Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4df253eb5790424e0b782eb86341d179db270ae9e5df7e3cf91efd3dd8a7d15f","abstract_canon_sha256":"587d10ea2701d486fac3c865053b98b448546c931bf35dbf87097e64deeb4ff4"},"schema_version":"1.0"},"canonical_sha256":"f325c540066693779fba1960c18684db375739ddceab23b3e930d0839ab7225c","source":{"kind":"arxiv","id":"1709.06636","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.06636","created_at":"2026-05-18T00:34:40Z"},{"alias_kind":"arxiv_version","alias_value":"1709.06636v1","created_at":"2026-05-18T00:34:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.06636","created_at":"2026-05-18T00:34:40Z"},{"alias_kind":"pith_short_12","alias_value":"6MS4KQAGM2JX","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6MS4KQAGM2JXPH52","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6MS4KQAG","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6MS4KQAGM2JXPH52DFQMDBUE3M","target":"record","payload":{"canonical_record":{"source":{"id":"1709.06636","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2017-09-19T20:30:30Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4df253eb5790424e0b782eb86341d179db270ae9e5df7e3cf91efd3dd8a7d15f","abstract_canon_sha256":"587d10ea2701d486fac3c865053b98b448546c931bf35dbf87097e64deeb4ff4"},"schema_version":"1.0"},"canonical_sha256":"f325c540066693779fba1960c18684db375739ddceab23b3e930d0839ab7225c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:40.902635Z","signature_b64":"DSGn7xsR4EaaKwHMK22E3PftLQPPE4SF0yZIlLIsOF/EIiuJ1yYfOCq1HmrbMjZHtFkAB7ATJSfe1pdi4SujCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f325c540066693779fba1960c18684db375739ddceab23b3e930d0839ab7225c","last_reissued_at":"2026-05-18T00:34:40.902003Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:40.902003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.06636","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":"2026-05-18T00:34:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PG9+uYu5gQ8YLkgIkQt1TFU0GvKu+NmlfhfcNUN8SSjfpHi2iws/YTrxyoYcpFTRaszTEvEhGEkIm6rIH9q1AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T06:04:09.409874Z"},"content_sha256":"a81244357f0d913cc550d26552397fbbe1566525aefae6063e6b6295d19396a1","schema_version":"1.0","event_id":"sha256:a81244357f0d913cc550d26552397fbbe1566525aefae6063e6b6295d19396a1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6MS4KQAGM2JXPH52DFQMDBUE3M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Attention-based Collaboration Framework for Multi-View Network Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.SI","authors_text":"Jian Tang, Jiawei Han, Jingbo Shang, Meng Qu, Ming Zhang, Xiang Ren","submitted_at":"2017-09-19T20:30:30Z","abstract_excerpt":"Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view repre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.06636","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":"2026-05-18T00:34:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cx+gQsvZiOEaKB7jk17+1MbeoWVU9Q5Cwc+UB8RWYYmSN8UGHAsuUYKVrawZLMMB7va2LnLXUV1YkwlrWXEnDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T06:04:09.410533Z"},"content_sha256":"92a071933c50415eb83b4f100627e8b6f3f98ad1fd732edf896e6600508efe57","schema_version":"1.0","event_id":"sha256:92a071933c50415eb83b4f100627e8b6f3f98ad1fd732edf896e6600508efe57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6MS4KQAGM2JXPH52DFQMDBUE3M/bundle.json","state_url":"https://pith.science/pith/6MS4KQAGM2JXPH52DFQMDBUE3M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6MS4KQAGM2JXPH52DFQMDBUE3M/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":"2026-06-07T06:04:09Z","links":{"resolver":"https://pith.science/pith/6MS4KQAGM2JXPH52DFQMDBUE3M","bundle":"https://pith.science/pith/6MS4KQAGM2JXPH52DFQMDBUE3M/bundle.json","state":"https://pith.science/pith/6MS4KQAGM2JXPH52DFQMDBUE3M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6MS4KQAGM2JXPH52DFQMDBUE3M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6MS4KQAGM2JXPH52DFQMDBUE3M","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":"587d10ea2701d486fac3c865053b98b448546c931bf35dbf87097e64deeb4ff4","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2017-09-19T20:30:30Z","title_canon_sha256":"4df253eb5790424e0b782eb86341d179db270ae9e5df7e3cf91efd3dd8a7d15f"},"schema_version":"1.0","source":{"id":"1709.06636","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.06636","created_at":"2026-05-18T00:34:40Z"},{"alias_kind":"arxiv_version","alias_value":"1709.06636v1","created_at":"2026-05-18T00:34:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.06636","created_at":"2026-05-18T00:34:40Z"},{"alias_kind":"pith_short_12","alias_value":"6MS4KQAGM2JX","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6MS4KQAGM2JXPH52","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6MS4KQAG","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:92a071933c50415eb83b4f100627e8b6f3f98ad1fd732edf896e6600508efe57","target":"graph","created_at":"2026-05-18T00:34:40Z","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":"Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view repre","authors_text":"Jian Tang, Jiawei Han, Jingbo Shang, Meng Qu, Ming Zhang, Xiang Ren","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2017-09-19T20:30:30Z","title":"An Attention-based Collaboration Framework for Multi-View Network Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.06636","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:a81244357f0d913cc550d26552397fbbe1566525aefae6063e6b6295d19396a1","target":"record","created_at":"2026-05-18T00:34:40Z","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":"587d10ea2701d486fac3c865053b98b448546c931bf35dbf87097e64deeb4ff4","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2017-09-19T20:30:30Z","title_canon_sha256":"4df253eb5790424e0b782eb86341d179db270ae9e5df7e3cf91efd3dd8a7d15f"},"schema_version":"1.0","source":{"id":"1709.06636","kind":"arxiv","version":1}},"canonical_sha256":"f325c540066693779fba1960c18684db375739ddceab23b3e930d0839ab7225c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f325c540066693779fba1960c18684db375739ddceab23b3e930d0839ab7225c","first_computed_at":"2026-05-18T00:34:40.902003Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:40.902003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DSGn7xsR4EaaKwHMK22E3PftLQPPE4SF0yZIlLIsOF/EIiuJ1yYfOCq1HmrbMjZHtFkAB7ATJSfe1pdi4SujCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:40.902635Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.06636","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a81244357f0d913cc550d26552397fbbe1566525aefae6063e6b6295d19396a1","sha256:92a071933c50415eb83b4f100627e8b6f3f98ad1fd732edf896e6600508efe57"],"state_sha256":"751d9c943034827e68afbbd003f6669ceee4d36325ae3669a8f31c6eaba53ae3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RTAckDPmDmoPNj70aDDba2DOQyeS7wfr5xVnHY1byvrt0/n8VPCInHWVqs4OGLk/T1PlN1b7Tju9/nbTdQR6BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T06:04:09.414238Z","bundle_sha256":"85e9887dfd1d60529648d8ef018299c4ceac3052c628a23c57cc955758cac0a1"}}