{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:Z6NB53MEO7XMQQNSVQVN5SEJB2","short_pith_number":"pith:Z6NB53ME","canonical_record":{"source":{"id":"1606.08658","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-28T11:37:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"259ccccc4268c0eedb80c126ef492a88ca9a73e1dfdf7e909b0addc8079a8064","abstract_canon_sha256":"1be8aa8423ca4cc103766dd67fa26158c7487f3b3109df7020fde1883993fdd2"},"schema_version":"1.0"},"canonical_sha256":"cf9a1eed8477eec841b2ac2adec8890e8ce2e035c597f931cf1c4ce2f3949225","source":{"kind":"arxiv","id":"1606.08658","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.08658","created_at":"2026-05-18T00:34:10Z"},{"alias_kind":"arxiv_version","alias_value":"1606.08658v3","created_at":"2026-05-18T00:34:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.08658","created_at":"2026-05-18T00:34:10Z"},{"alias_kind":"pith_short_12","alias_value":"Z6NB53MEO7XM","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"Z6NB53MEO7XMQQNS","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"Z6NB53ME","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:Z6NB53MEO7XMQQNSVQVN5SEJB2","target":"record","payload":{"canonical_record":{"source":{"id":"1606.08658","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-28T11:37:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"259ccccc4268c0eedb80c126ef492a88ca9a73e1dfdf7e909b0addc8079a8064","abstract_canon_sha256":"1be8aa8423ca4cc103766dd67fa26158c7487f3b3109df7020fde1883993fdd2"},"schema_version":"1.0"},"canonical_sha256":"cf9a1eed8477eec841b2ac2adec8890e8ce2e035c597f931cf1c4ce2f3949225","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:10.392058Z","signature_b64":"zSJ4QeJB6lNSZdUXX+eOR+qn2UNpVWuorg9T6OgoFwiS4H4LUnB7sCRsbp3YGbPHCTSjbV4PSxl1NMxGXoA7Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf9a1eed8477eec841b2ac2adec8890e8ce2e035c597f931cf1c4ce2f3949225","last_reissued_at":"2026-05-18T00:34:10.391393Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:10.391393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.08658","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:34:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XknNKc8PpvC9XJLqPhJDCzy4jwrmTHcYJ1XUfSBxqfxqEA+5m7GRQPyaOFIpStU/e4aT6b/3Eyn+L37TbceVBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T00:42:04.170294Z"},"content_sha256":"4d62833a34cfc3a0bce5eb250abeae52f27a088a89111c770eb34d0335e9d335","schema_version":"1.0","event_id":"sha256:4d62833a34cfc3a0bce5eb250abeae52f27a088a89111c770eb34d0335e9d335"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:Z6NB53MEO7XMQQNSVQVN5SEJB2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hendrik Blockeel, Sebastijan Dumancic","submitted_at":"2016-06-28T11:37:45Z","abstract_excerpt":"The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier. Existing methods typically focus on vectorized data and offer little support for relational data, which additionally describe relationships among instances. In this work we introduce an approach for relational unsupervised representation learning. Viewing a relational dataset as a hypergraph, new features are obtained by clustering vertices and hyperedges. To find a representation suited for many relational learning tasks, a wide range of similarit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.08658","kind":"arxiv","version":3},"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":"2026-05-18T00:34:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5vxh0GHxs70Rem/Ul8ryJyt/tfzFVZqo+EQ/ygR/Mq6gpzuyucxPZzkHq8tNbvWRUXAodrjsrflwgQIFCG6yDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T00:42:04.171011Z"},"content_sha256":"04a7f484e3b01aaf6b9cfe608f0a54b9f101de464d1e013b718733ac39aab2c5","schema_version":"1.0","event_id":"sha256:04a7f484e3b01aaf6b9cfe608f0a54b9f101de464d1e013b718733ac39aab2c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z6NB53MEO7XMQQNSVQVN5SEJB2/bundle.json","state_url":"https://pith.science/pith/Z6NB53MEO7XMQQNSVQVN5SEJB2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z6NB53MEO7XMQQNSVQVN5SEJB2/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":"2026-06-01T00:42:04Z","links":{"resolver":"https://pith.science/pith/Z6NB53MEO7XMQQNSVQVN5SEJB2","bundle":"https://pith.science/pith/Z6NB53MEO7XMQQNSVQVN5SEJB2/bundle.json","state":"https://pith.science/pith/Z6NB53MEO7XMQQNSVQVN5SEJB2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z6NB53MEO7XMQQNSVQVN5SEJB2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:Z6NB53MEO7XMQQNSVQVN5SEJB2","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":"1be8aa8423ca4cc103766dd67fa26158c7487f3b3109df7020fde1883993fdd2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-28T11:37:45Z","title_canon_sha256":"259ccccc4268c0eedb80c126ef492a88ca9a73e1dfdf7e909b0addc8079a8064"},"schema_version":"1.0","source":{"id":"1606.08658","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.08658","created_at":"2026-05-18T00:34:10Z"},{"alias_kind":"arxiv_version","alias_value":"1606.08658v3","created_at":"2026-05-18T00:34:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.08658","created_at":"2026-05-18T00:34:10Z"},{"alias_kind":"pith_short_12","alias_value":"Z6NB53MEO7XM","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"Z6NB53MEO7XMQQNS","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"Z6NB53ME","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:04a7f484e3b01aaf6b9cfe608f0a54b9f101de464d1e013b718733ac39aab2c5","target":"graph","created_at":"2026-05-18T00:34:10Z","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":"The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier. Existing methods typically focus on vectorized data and offer little support for relational data, which additionally describe relationships among instances. In this work we introduce an approach for relational unsupervised representation learning. Viewing a relational dataset as a hypergraph, new features are obtained by clustering vertices and hyperedges. To find a representation suited for many relational learning tasks, a wide range of similarit","authors_text":"Hendrik Blockeel, Sebastijan Dumancic","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-28T11:37:45Z","title":"Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.08658","kind":"arxiv","version":3},"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":"sha256:4d62833a34cfc3a0bce5eb250abeae52f27a088a89111c770eb34d0335e9d335","target":"record","created_at":"2026-05-18T00:34:10Z","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":"1be8aa8423ca4cc103766dd67fa26158c7487f3b3109df7020fde1883993fdd2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-28T11:37:45Z","title_canon_sha256":"259ccccc4268c0eedb80c126ef492a88ca9a73e1dfdf7e909b0addc8079a8064"},"schema_version":"1.0","source":{"id":"1606.08658","kind":"arxiv","version":3}},"canonical_sha256":"cf9a1eed8477eec841b2ac2adec8890e8ce2e035c597f931cf1c4ce2f3949225","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cf9a1eed8477eec841b2ac2adec8890e8ce2e035c597f931cf1c4ce2f3949225","first_computed_at":"2026-05-18T00:34:10.391393Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:10.391393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zSJ4QeJB6lNSZdUXX+eOR+qn2UNpVWuorg9T6OgoFwiS4H4LUnB7sCRsbp3YGbPHCTSjbV4PSxl1NMxGXoA7Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:10.392058Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.08658","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d62833a34cfc3a0bce5eb250abeae52f27a088a89111c770eb34d0335e9d335","sha256:04a7f484e3b01aaf6b9cfe608f0a54b9f101de464d1e013b718733ac39aab2c5"],"state_sha256":"1b7446a9629db67ee685ddee83e85ada26f7ffbedeccaf1d9208d8a412bb714c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0KsXUlNQC2u4uyAENppQS1cjgk9Qk8GsNYxFusUcy1LU6SDA7aYk9vG+noDSQhW11WevTHI6mF16HXlvpP74CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T00:42:04.175436Z","bundle_sha256":"eaf25c866b6ecda9ba9c6d2266f1e60e2a1a846b28e95b72b29fd18076244e6b"}}