{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:FYQR3QZLB5YOXMHNR72Z53K52O","short_pith_number":"pith:FYQR3QZL","canonical_record":{"source":{"id":"2107.01319","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-03T01:28:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5c4cdd59b17179a41ea83cbcd862e9c0b0812dadb26c2c3f541a38b0c62f3e54","abstract_canon_sha256":"9cabbb77f5d84264c7eaf22ecc6603eb1f0fb0d7a2f09ac7a742b48b128194d3"},"schema_version":"1.0"},"canonical_sha256":"2e211dc32b0f70ebb0ed8ff59eed5dd39552f04816b29a7396d0b0c82076a53e","source":{"kind":"arxiv","id":"2107.01319","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.01319","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"arxiv_version","alias_value":"2107.01319v2","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.01319","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"pith_short_12","alias_value":"FYQR3QZLB5YO","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"pith_short_16","alias_value":"FYQR3QZLB5YOXMHN","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"pith_short_8","alias_value":"FYQR3QZL","created_at":"2026-07-05T02:58:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:FYQR3QZLB5YOXMHNR72Z53K52O","target":"record","payload":{"canonical_record":{"source":{"id":"2107.01319","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-03T01:28:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5c4cdd59b17179a41ea83cbcd862e9c0b0812dadb26c2c3f541a38b0c62f3e54","abstract_canon_sha256":"9cabbb77f5d84264c7eaf22ecc6603eb1f0fb0d7a2f09ac7a742b48b128194d3"},"schema_version":"1.0"},"canonical_sha256":"2e211dc32b0f70ebb0ed8ff59eed5dd39552f04816b29a7396d0b0c82076a53e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:58:43.596960Z","signature_b64":"vbtFYoW1z8TJkm8BJBjp8THw5w1HIQRLR7oOLoyCxJGkXAJi3Mpw1XqRQZWOf5i2OcNWt7hJPGfB7m3yVFt/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e211dc32b0f70ebb0ed8ff59eed5dd39552f04816b29a7396d0b0c82076a53e","last_reissued_at":"2026-07-05T02:58:43.596475Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:58:43.596475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.01319","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":"2026-07-05T02:58:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"97IaUi3ucK0Vc9whZcDNmdzNRUKr8e3L2WpqLpJzv8aEGyallOxOFyWz3BpL+Wy/FLCS/sKbicdF/RMgtJa7AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:15:00.519919Z"},"content_sha256":"f46b282c5d914c1026ef1faa54858e565508fc921d47fc97f564f179c137ff3f","schema_version":"1.0","event_id":"sha256:f46b282c5d914c1026ef1faa54858e565508fc921d47fc97f564f179c137ff3f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:FYQR3QZLB5YOXMHNR72Z53K52O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Hierarchical Graph Neural Networks for Image Clustering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"David Wipf, Stefano Soatto, Tianjun Xiao, Tong He, Wei Xia, Yifan Xing, Yongxin Wang, Yuanjun Xiong, Zheng Zhang","submitted_at":"2021-07-03T01:28:42Z","abstract_excerpt":"We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated with labels belonging to a disjoint set of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level. Unlike fully unsupervised hierarchical clustering, the choice of grouping and complexity criteria stems naturally from supervision in the training set. The resulting method, Hi-LANDER, achieves an average of 54% "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.01319","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.01319/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T02:58:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WffNhAkkiNaDujhJ283PwFhsWHBrGBdiCUElZxdXh2QZpq0x1CUrV3WW+Zyhg3LnqvzbhpRrt6OKHqmg5iH1Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:15:00.521005Z"},"content_sha256":"6740901e7ad845230f704f8009543f2d614267f5387972a56665ee580ecb44ec","schema_version":"1.0","event_id":"sha256:6740901e7ad845230f704f8009543f2d614267f5387972a56665ee580ecb44ec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FYQR3QZLB5YOXMHNR72Z53K52O/bundle.json","state_url":"https://pith.science/pith/FYQR3QZLB5YOXMHNR72Z53K52O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FYQR3QZLB5YOXMHNR72Z53K52O/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":"2026-08-04T15:15:00Z","links":{"resolver":"https://pith.science/pith/FYQR3QZLB5YOXMHNR72Z53K52O","bundle":"https://pith.science/pith/FYQR3QZLB5YOXMHNR72Z53K52O/bundle.json","state":"https://pith.science/pith/FYQR3QZLB5YOXMHNR72Z53K52O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FYQR3QZLB5YOXMHNR72Z53K52O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:FYQR3QZLB5YOXMHNR72Z53K52O","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":"9cabbb77f5d84264c7eaf22ecc6603eb1f0fb0d7a2f09ac7a742b48b128194d3","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-03T01:28:42Z","title_canon_sha256":"5c4cdd59b17179a41ea83cbcd862e9c0b0812dadb26c2c3f541a38b0c62f3e54"},"schema_version":"1.0","source":{"id":"2107.01319","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.01319","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"arxiv_version","alias_value":"2107.01319v2","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.01319","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"pith_short_12","alias_value":"FYQR3QZLB5YO","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"pith_short_16","alias_value":"FYQR3QZLB5YOXMHN","created_at":"2026-07-05T02:58:43Z"},{"alias_kind":"pith_short_8","alias_value":"FYQR3QZL","created_at":"2026-07-05T02:58:43Z"}],"graph_snapshots":[{"event_id":"sha256:6740901e7ad845230f704f8009543f2d614267f5387972a56665ee580ecb44ec","target":"graph","created_at":"2026-07-05T02:58:43Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.01319/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated with labels belonging to a disjoint set of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level. Unlike fully unsupervised hierarchical clustering, the choice of grouping and complexity criteria stems naturally from supervision in the training set. The resulting method, Hi-LANDER, achieves an average of 54% ","authors_text":"David Wipf, Stefano Soatto, Tianjun Xiao, Tong He, Wei Xia, Yifan Xing, Yongxin Wang, Yuanjun Xiong, Zheng Zhang","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-03T01:28:42Z","title":"Learning Hierarchical Graph Neural Networks for Image Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.01319","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:f46b282c5d914c1026ef1faa54858e565508fc921d47fc97f564f179c137ff3f","target":"record","created_at":"2026-07-05T02:58:43Z","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":"9cabbb77f5d84264c7eaf22ecc6603eb1f0fb0d7a2f09ac7a742b48b128194d3","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-03T01:28:42Z","title_canon_sha256":"5c4cdd59b17179a41ea83cbcd862e9c0b0812dadb26c2c3f541a38b0c62f3e54"},"schema_version":"1.0","source":{"id":"2107.01319","kind":"arxiv","version":2}},"canonical_sha256":"2e211dc32b0f70ebb0ed8ff59eed5dd39552f04816b29a7396d0b0c82076a53e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e211dc32b0f70ebb0ed8ff59eed5dd39552f04816b29a7396d0b0c82076a53e","first_computed_at":"2026-07-05T02:58:43.596475Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:58:43.596475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vbtFYoW1z8TJkm8BJBjp8THw5w1HIQRLR7oOLoyCxJGkXAJi3Mpw1XqRQZWOf5i2OcNWt7hJPGfB7m3yVFt/Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:58:43.596960Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.01319","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f46b282c5d914c1026ef1faa54858e565508fc921d47fc97f564f179c137ff3f","sha256:6740901e7ad845230f704f8009543f2d614267f5387972a56665ee580ecb44ec"],"state_sha256":"0905644de542a7c41d2d4edb9eb5679c645067038033ffba4f917543a39e70be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8bxYGubBg7XSqqlfL3gXGJOTWp282mPIFfqcsfaMJLaPj1LctHQS/HI+SOZpJLvdS81O7CdFC+1DnEkZglAbDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T15:15:00.531305Z","bundle_sha256":"ccb8aa40d8ae5d405b93243dfb540e735858a70cdbe2f4842a991a831885b6f2"}}