{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:6UI4DS67OH7N7CNNJB6B4CMMXS","short_pith_number":"pith:6UI4DS67","canonical_record":{"source":{"id":"1608.04862","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-08-17T06:01:29Z","cross_cats_sorted":["physics.soc-ph"],"title_canon_sha256":"9cde882ef8632d559646ec54ab15678ad97c4fa874c7b4a40c21a49bf3f4d422","abstract_canon_sha256":"d01a5e52abafa8c497d8f2b27eed16963ff7cb30d2172a48ebdc26655c0f366e"},"schema_version":"1.0"},"canonical_sha256":"f511c1cbdf71fedf89ad487c1e098cbcbf158a0533c05a6e5f4bffce2f1eca8c","source":{"kind":"arxiv","id":"1608.04862","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.04862","created_at":"2026-05-18T01:07:20Z"},{"alias_kind":"arxiv_version","alias_value":"1608.04862v2","created_at":"2026-05-18T01:07:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04862","created_at":"2026-05-18T01:07:20Z"},{"alias_kind":"pith_short_12","alias_value":"6UI4DS67OH7N","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"6UI4DS67OH7N7CNN","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"6UI4DS67","created_at":"2026-05-18T12:30:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:6UI4DS67OH7N7CNNJB6B4CMMXS","target":"record","payload":{"canonical_record":{"source":{"id":"1608.04862","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-08-17T06:01:29Z","cross_cats_sorted":["physics.soc-ph"],"title_canon_sha256":"9cde882ef8632d559646ec54ab15678ad97c4fa874c7b4a40c21a49bf3f4d422","abstract_canon_sha256":"d01a5e52abafa8c497d8f2b27eed16963ff7cb30d2172a48ebdc26655c0f366e"},"schema_version":"1.0"},"canonical_sha256":"f511c1cbdf71fedf89ad487c1e098cbcbf158a0533c05a6e5f4bffce2f1eca8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:07:20.096654Z","signature_b64":"hyAwUlZidGd3wg6R0urOhLUmYuJKchU08iBR++l/KNnpdg7YD8UHz9sLpBa919AYptvB4EZd9rlEwsGAMoDsAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f511c1cbdf71fedf89ad487c1e098cbcbf158a0533c05a6e5f4bffce2f1eca8c","last_reissued_at":"2026-05-18T01:07:20.096052Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:07:20.096052Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.04862","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":"2026-05-18T01:07:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fqaOKkX5DVW1GGrHtediYpwTMiIkpHEIzlQGMWhkBWAxuJtmcyjBD80JGCqegDcn9T3qqCYvCcb8uw9c4IvZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T10:21:01.726626Z"},"content_sha256":"d45ddbae119eebd0e7c4439e354907facb5510b0f06385fa524153af06a33186","schema_version":"1.0","event_id":"sha256:d45ddbae119eebd0e7c4439e354907facb5510b0f06385fa524153af06a33186"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:6UI4DS67OH7N7CNNJB6B4CMMXS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature Driven and Point Process Approaches for Popularity Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Lexing Xie, Marian-Andrei Rizoiu, Swapnil Mishra","submitted_at":"2016-08-17T06:01:29Z","abstract_excerpt":"Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with a new hybrid approach and a new performance benchmark. We model each social cascade with a marked Hawkes self-exciting point process, and estimate the content virality, memory decay, and user influence. We then learn a predictive layer for popularit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04862","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":""},"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":"2026-05-18T01:07:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G1Rr6VjHHFe0iimtI6/61uH9UJRm2POj+97ByyFmY/mCcwSW4np78h2xZzUllBwgVf7EfFbnFj+f9DjXcMIMCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T10:21:01.727257Z"},"content_sha256":"38828f90f21c6867d3fe874253704ff76a43e7b38cb1f92d04ddbdeb61b1721b","schema_version":"1.0","event_id":"sha256:38828f90f21c6867d3fe874253704ff76a43e7b38cb1f92d04ddbdeb61b1721b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6UI4DS67OH7N7CNNJB6B4CMMXS/bundle.json","state_url":"https://pith.science/pith/6UI4DS67OH7N7CNNJB6B4CMMXS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6UI4DS67OH7N7CNNJB6B4CMMXS/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":"2026-05-25T10:21:01Z","links":{"resolver":"https://pith.science/pith/6UI4DS67OH7N7CNNJB6B4CMMXS","bundle":"https://pith.science/pith/6UI4DS67OH7N7CNNJB6B4CMMXS/bundle.json","state":"https://pith.science/pith/6UI4DS67OH7N7CNNJB6B4CMMXS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6UI4DS67OH7N7CNNJB6B4CMMXS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:6UI4DS67OH7N7CNNJB6B4CMMXS","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":"d01a5e52abafa8c497d8f2b27eed16963ff7cb30d2172a48ebdc26655c0f366e","cross_cats_sorted":["physics.soc-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-08-17T06:01:29Z","title_canon_sha256":"9cde882ef8632d559646ec54ab15678ad97c4fa874c7b4a40c21a49bf3f4d422"},"schema_version":"1.0","source":{"id":"1608.04862","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.04862","created_at":"2026-05-18T01:07:20Z"},{"alias_kind":"arxiv_version","alias_value":"1608.04862v2","created_at":"2026-05-18T01:07:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04862","created_at":"2026-05-18T01:07:20Z"},{"alias_kind":"pith_short_12","alias_value":"6UI4DS67OH7N","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"6UI4DS67OH7N7CNN","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"6UI4DS67","created_at":"2026-05-18T12:30:04Z"}],"graph_snapshots":[{"event_id":"sha256:38828f90f21c6867d3fe874253704ff76a43e7b38cb1f92d04ddbdeb61b1721b","target":"graph","created_at":"2026-05-18T01:07:20Z","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":"Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with a new hybrid approach and a new performance benchmark. We model each social cascade with a marked Hawkes self-exciting point process, and estimate the content virality, memory decay, and user influence. We then learn a predictive layer for popularit","authors_text":"Lexing Xie, Marian-Andrei Rizoiu, Swapnil Mishra","cross_cats":["physics.soc-ph"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-08-17T06:01:29Z","title":"Feature Driven and Point Process Approaches for Popularity Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04862","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:d45ddbae119eebd0e7c4439e354907facb5510b0f06385fa524153af06a33186","target":"record","created_at":"2026-05-18T01:07:20Z","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":"d01a5e52abafa8c497d8f2b27eed16963ff7cb30d2172a48ebdc26655c0f366e","cross_cats_sorted":["physics.soc-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-08-17T06:01:29Z","title_canon_sha256":"9cde882ef8632d559646ec54ab15678ad97c4fa874c7b4a40c21a49bf3f4d422"},"schema_version":"1.0","source":{"id":"1608.04862","kind":"arxiv","version":2}},"canonical_sha256":"f511c1cbdf71fedf89ad487c1e098cbcbf158a0533c05a6e5f4bffce2f1eca8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f511c1cbdf71fedf89ad487c1e098cbcbf158a0533c05a6e5f4bffce2f1eca8c","first_computed_at":"2026-05-18T01:07:20.096052Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:07:20.096052Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hyAwUlZidGd3wg6R0urOhLUmYuJKchU08iBR++l/KNnpdg7YD8UHz9sLpBa919AYptvB4EZd9rlEwsGAMoDsAw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:07:20.096654Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.04862","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d45ddbae119eebd0e7c4439e354907facb5510b0f06385fa524153af06a33186","sha256:38828f90f21c6867d3fe874253704ff76a43e7b38cb1f92d04ddbdeb61b1721b"],"state_sha256":"8fa859c9c9a5bd6693af94f9a11e6c37a3da970e3ff17ca501254ef7e9f01755"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FyOoetNpDyRjNyl6ZV8Gpjjd3n7qF5Ol0A8dx8E0vGnpvkus+WLI2VcbCJ6L63j+PJ1FJwrYdZM828OSwIhkAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T10:21:01.730696Z","bundle_sha256":"8afd2425da9f80f56d2f2efa2d485b27f363e78e2d7ecdcd39f880fe30279b4a"}}