{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:C632KC4DFZIND7IGB4T6DVQU3Z","short_pith_number":"pith:C632KC4D","canonical_record":{"source":{"id":"2305.16886","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T12:45:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7b72f56babc67d9c838820429378110b17992398116ebc4575c9d0ef29306ec9","abstract_canon_sha256":"b1a5aff5826bc45369cfdbc3fb211a626668b788d4dbc0b6f067ab73daad9473"},"schema_version":"1.0"},"canonical_sha256":"17b7a50b832e50d1fd060f27e1d614de4c078f406ed506ab855d915880583422","source":{"kind":"arxiv","id":"2305.16886","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.16886","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"arxiv_version","alias_value":"2305.16886v2","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16886","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"pith_short_12","alias_value":"C632KC4DFZIN","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"pith_short_16","alias_value":"C632KC4DFZIND7IG","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"pith_short_8","alias_value":"C632KC4D","created_at":"2026-07-05T08:11:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:C632KC4DFZIND7IGB4T6DVQU3Z","target":"record","payload":{"canonical_record":{"source":{"id":"2305.16886","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T12:45:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7b72f56babc67d9c838820429378110b17992398116ebc4575c9d0ef29306ec9","abstract_canon_sha256":"b1a5aff5826bc45369cfdbc3fb211a626668b788d4dbc0b6f067ab73daad9473"},"schema_version":"1.0"},"canonical_sha256":"17b7a50b832e50d1fd060f27e1d614de4c078f406ed506ab855d915880583422","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:53.014713Z","signature_b64":"lV90dn7vfR8h+jqEUVupLKLlKGbIMr7lrex1scmzynXFtohjyWOx71O8mGSlYy2dPp1VBstYWtUJC9plpUkeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17b7a50b832e50d1fd060f27e1d614de4c078f406ed506ab855d915880583422","last_reissued_at":"2026-07-05T08:11:53.014237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:53.014237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.16886","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":"2026-07-05T08:11:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FARSMq/HrVZ+yLlDDiEs6yvQbDCGdvH9sjrudBYdciwJwnrc9nIvIu6xUYokgD0BD9svWATMFMnKMSp0nD+4Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T11:26:37.846937Z"},"content_sha256":"4c9cbef3e55f5da6f629e4950dcd90b14fde488256dba4a650485972169fc1d0","schema_version":"1.0","event_id":"sha256:4c9cbef3e55f5da6f629e4950dcd90b14fde488256dba4a650485972169fc1d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:C632KC4DFZIND7IGB4T6DVQU3Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Doina Bucur, Elia Cunegatti, Giovanni Iacca, Matteo Farina","submitted_at":"2023-05-26T12:45:58Z","abstract_excerpt":"Pruning-at-Initialization (PaI) algorithms provide Sparse Neural Networks (SNNs) which are computationally more efficient than their dense counterparts, and try to avoid performance degradation. While much emphasis has been directed towards \\emph{how} to prune, we still do not know \\emph{what topological metrics} of the SNNs characterize \\emph{good performance}. From prior work, we have layer-wise topological metrics by which SNN performance can be predicted: the Ramanujan-based metrics. To exploit these metrics, proper ways to represent network layers via Graph Encodings (GEs) are needed, wit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16886","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/2305.16886/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":"2026-07-05T08:11:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WOGW1MEdwTpQZ2AoQvpPCjVTI0FVvAM1+kB+sLpdaSVPXPmiPfjnXahI0xqG81+mOigyYxHa/NGKZ6eIZQ0WDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T11:26:37.847455Z"},"content_sha256":"d845119a7baa5f91126fcda51de1dc4be4edc50f841f17ead63250a4f293e2d8","schema_version":"1.0","event_id":"sha256:d845119a7baa5f91126fcda51de1dc4be4edc50f841f17ead63250a4f293e2d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C632KC4DFZIND7IGB4T6DVQU3Z/bundle.json","state_url":"https://pith.science/pith/C632KC4DFZIND7IGB4T6DVQU3Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C632KC4DFZIND7IGB4T6DVQU3Z/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":"2026-08-23T11:26:37Z","links":{"resolver":"https://pith.science/pith/C632KC4DFZIND7IGB4T6DVQU3Z","bundle":"https://pith.science/pith/C632KC4DFZIND7IGB4T6DVQU3Z/bundle.json","state":"https://pith.science/pith/C632KC4DFZIND7IGB4T6DVQU3Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C632KC4DFZIND7IGB4T6DVQU3Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:C632KC4DFZIND7IGB4T6DVQU3Z","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":"b1a5aff5826bc45369cfdbc3fb211a626668b788d4dbc0b6f067ab73daad9473","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T12:45:58Z","title_canon_sha256":"7b72f56babc67d9c838820429378110b17992398116ebc4575c9d0ef29306ec9"},"schema_version":"1.0","source":{"id":"2305.16886","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.16886","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"arxiv_version","alias_value":"2305.16886v2","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16886","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"pith_short_12","alias_value":"C632KC4DFZIN","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"pith_short_16","alias_value":"C632KC4DFZIND7IG","created_at":"2026-07-05T08:11:53Z"},{"alias_kind":"pith_short_8","alias_value":"C632KC4D","created_at":"2026-07-05T08:11:53Z"}],"graph_snapshots":[{"event_id":"sha256:d845119a7baa5f91126fcda51de1dc4be4edc50f841f17ead63250a4f293e2d8","target":"graph","created_at":"2026-07-05T08:11:53Z","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/2305.16886/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pruning-at-Initialization (PaI) algorithms provide Sparse Neural Networks (SNNs) which are computationally more efficient than their dense counterparts, and try to avoid performance degradation. While much emphasis has been directed towards \\emph{how} to prune, we still do not know \\emph{what topological metrics} of the SNNs characterize \\emph{good performance}. From prior work, we have layer-wise topological metrics by which SNN performance can be predicted: the Ramanujan-based metrics. To exploit these metrics, proper ways to represent network layers via Graph Encodings (GEs) are needed, wit","authors_text":"Doina Bucur, Elia Cunegatti, Giovanni Iacca, Matteo Farina","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T12:45:58Z","title":"Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16886","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":"sha256:4c9cbef3e55f5da6f629e4950dcd90b14fde488256dba4a650485972169fc1d0","target":"record","created_at":"2026-07-05T08:11:53Z","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":"b1a5aff5826bc45369cfdbc3fb211a626668b788d4dbc0b6f067ab73daad9473","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T12:45:58Z","title_canon_sha256":"7b72f56babc67d9c838820429378110b17992398116ebc4575c9d0ef29306ec9"},"schema_version":"1.0","source":{"id":"2305.16886","kind":"arxiv","version":2}},"canonical_sha256":"17b7a50b832e50d1fd060f27e1d614de4c078f406ed506ab855d915880583422","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"17b7a50b832e50d1fd060f27e1d614de4c078f406ed506ab855d915880583422","first_computed_at":"2026-07-05T08:11:53.014237Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:11:53.014237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lV90dn7vfR8h+jqEUVupLKLlKGbIMr7lrex1scmzynXFtohjyWOx71O8mGSlYy2dPp1VBstYWtUJC9plpUkeAg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:11:53.014713Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.16886","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c9cbef3e55f5da6f629e4950dcd90b14fde488256dba4a650485972169fc1d0","sha256:d845119a7baa5f91126fcda51de1dc4be4edc50f841f17ead63250a4f293e2d8"],"state_sha256":"e8e5b6a542535f15afb9bcc0890bbd414da46851642c080d7d8ffa24c76eb547"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"66OxK8gH4uUHWSKW0gyZjyyfDtyR4HwTzYkMv3BYRQUlGqMA4y+U4+UTVayu6HXmOVhxH8NNBNM01/for0wiCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T11:26:37.851994Z","bundle_sha256":"8731204e64085d68175254501150c935881db1606756278ac178a4f254e341ef"}}