{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:JONBFCQANXZTEKMSACGRJCCAOJ","short_pith_number":"pith:JONBFCQA","canonical_record":{"source":{"id":"1903.07299","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-18T08:37:13Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"0329c09ca28de753586a9598f7f2140309623317e1d916d7363b446d575b60db","abstract_canon_sha256":"d0e2e489324059226f5d3334f0ca2c84710588df6e82e6cbf0ae0dc079570fba"},"schema_version":"1.0"},"canonical_sha256":"4b9a128a006df3322992008d148840727dd385351973acf3f9dab5217256249f","source":{"kind":"arxiv","id":"1903.07299","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.07299","created_at":"2026-05-17T23:51:01Z"},{"alias_kind":"arxiv_version","alias_value":"1903.07299v1","created_at":"2026-05-17T23:51:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.07299","created_at":"2026-05-17T23:51:01Z"},{"alias_kind":"pith_short_12","alias_value":"JONBFCQANXZT","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"JONBFCQANXZTEKMS","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"JONBFCQA","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:JONBFCQANXZTEKMSACGRJCCAOJ","target":"record","payload":{"canonical_record":{"source":{"id":"1903.07299","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-18T08:37:13Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"0329c09ca28de753586a9598f7f2140309623317e1d916d7363b446d575b60db","abstract_canon_sha256":"d0e2e489324059226f5d3334f0ca2c84710588df6e82e6cbf0ae0dc079570fba"},"schema_version":"1.0"},"canonical_sha256":"4b9a128a006df3322992008d148840727dd385351973acf3f9dab5217256249f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:01.822112Z","signature_b64":"XOFzxorh/FuQVLwCD4aw+HmRznRFGyUt/dWg7oRqToCszYfV9BBJY2YTFOD2tarBtgeHXxcMo3IoWUuQ2F7ABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b9a128a006df3322992008d148840727dd385351973acf3f9dab5217256249f","last_reissued_at":"2026-05-17T23:51:01.821413Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:01.821413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.07299","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":"2026-05-17T23:51:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JoNBfIBPhCSjJDZ+4lCMyKbzo9Llei1k3Sb5MNrgNxWZ2pOHu851p/vSK+5x/bwiKhEBe/FChxSsqMZPSzxrAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T15:52:27.760378Z"},"content_sha256":"fbc8fe4e5c2729316e010a06473a3c1e6936b6c9dd15c2fa78835e0f83146c99","schema_version":"1.0","event_id":"sha256:fbc8fe4e5c2729316e010a06473a3c1e6936b6c9dd15c2fa78835e0f83146c99"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:JONBFCQANXZTEKMSACGRJCCAOJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Autoregressive Models for Sequences of Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cesare Alippi, Daniele Grattarola, Daniele Zambon, Lorenzo Livi","submitted_at":"2019-03-18T08:37:13Z","abstract_excerpt":"This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very general family of graphs, characterised by a variable topology, and attributes associated with nodes and edges. A graph neural network (GNN) is also proposed to learn the AR function associated with the graph-generating process (GGP), and subsequently predict the next graph in a sequence. The proposed method is compared with four baselines on synthetic GGPs, denoting a significantly better performance on all considere"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.07299","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":"2026-05-17T23:51:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lTLsipoGM4HNfGv7hDG3I/dWlnw1vptULwH7slXz6mq7w+FpxrnE4v+B6xnRokBV8/OogbrORhJUQ9nRaTBPBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T15:52:27.761032Z"},"content_sha256":"576907bc4435e8326f09858c756ae715462e752707d6c7cd4e1f8004bb7f768e","schema_version":"1.0","event_id":"sha256:576907bc4435e8326f09858c756ae715462e752707d6c7cd4e1f8004bb7f768e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JONBFCQANXZTEKMSACGRJCCAOJ/bundle.json","state_url":"https://pith.science/pith/JONBFCQANXZTEKMSACGRJCCAOJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JONBFCQANXZTEKMSACGRJCCAOJ/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":"2026-06-02T15:52:27Z","links":{"resolver":"https://pith.science/pith/JONBFCQANXZTEKMSACGRJCCAOJ","bundle":"https://pith.science/pith/JONBFCQANXZTEKMSACGRJCCAOJ/bundle.json","state":"https://pith.science/pith/JONBFCQANXZTEKMSACGRJCCAOJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JONBFCQANXZTEKMSACGRJCCAOJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:JONBFCQANXZTEKMSACGRJCCAOJ","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":"d0e2e489324059226f5d3334f0ca2c84710588df6e82e6cbf0ae0dc079570fba","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-18T08:37:13Z","title_canon_sha256":"0329c09ca28de753586a9598f7f2140309623317e1d916d7363b446d575b60db"},"schema_version":"1.0","source":{"id":"1903.07299","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.07299","created_at":"2026-05-17T23:51:01Z"},{"alias_kind":"arxiv_version","alias_value":"1903.07299v1","created_at":"2026-05-17T23:51:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.07299","created_at":"2026-05-17T23:51:01Z"},{"alias_kind":"pith_short_12","alias_value":"JONBFCQANXZT","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"JONBFCQANXZTEKMS","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"JONBFCQA","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:576907bc4435e8326f09858c756ae715462e752707d6c7cd4e1f8004bb7f768e","target":"graph","created_at":"2026-05-17T23:51:01Z","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":"This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very general family of graphs, characterised by a variable topology, and attributes associated with nodes and edges. A graph neural network (GNN) is also proposed to learn the AR function associated with the graph-generating process (GGP), and subsequently predict the next graph in a sequence. The proposed method is compared with four baselines on synthetic GGPs, denoting a significantly better performance on all considere","authors_text":"Cesare Alippi, Daniele Grattarola, Daniele Zambon, Lorenzo Livi","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-18T08:37:13Z","title":"Autoregressive Models for Sequences of Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.07299","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:fbc8fe4e5c2729316e010a06473a3c1e6936b6c9dd15c2fa78835e0f83146c99","target":"record","created_at":"2026-05-17T23:51:01Z","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":"d0e2e489324059226f5d3334f0ca2c84710588df6e82e6cbf0ae0dc079570fba","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-18T08:37:13Z","title_canon_sha256":"0329c09ca28de753586a9598f7f2140309623317e1d916d7363b446d575b60db"},"schema_version":"1.0","source":{"id":"1903.07299","kind":"arxiv","version":1}},"canonical_sha256":"4b9a128a006df3322992008d148840727dd385351973acf3f9dab5217256249f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b9a128a006df3322992008d148840727dd385351973acf3f9dab5217256249f","first_computed_at":"2026-05-17T23:51:01.821413Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:01.821413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XOFzxorh/FuQVLwCD4aw+HmRznRFGyUt/dWg7oRqToCszYfV9BBJY2YTFOD2tarBtgeHXxcMo3IoWUuQ2F7ABA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:01.822112Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.07299","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbc8fe4e5c2729316e010a06473a3c1e6936b6c9dd15c2fa78835e0f83146c99","sha256:576907bc4435e8326f09858c756ae715462e752707d6c7cd4e1f8004bb7f768e"],"state_sha256":"ecf4730126b31ff1dd2461895da575003875b1dee849a825b7a9942814f9540d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tVkZ9eT2M6m1jKjNwX+YXG2qK49aGC9A33Q3ov13tNsitfdNVLdxN9Hl3XqS7z7DoG0rmmNccqtR07r3emmkBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T15:52:27.764071Z","bundle_sha256":"1e128c3d25276649b901afbcafec8c1dab51e0d76b14b4d712748f2bb3dc9add"}}