{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5TS7F6C5U2ES47LUK5BZQ7JD63","short_pith_number":"pith:5TS7F6C5","canonical_record":{"source":{"id":"1802.04944","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-14T03:52:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e266f77533cbd054f529a1699c55bba1f6ccfd1c270596749b8d9cc9c27ec83f","abstract_canon_sha256":"aca46a1a8af0da111012cb992c0d0f34ca5a4273d20ecb855e062c778bedbab0"},"schema_version":"1.0"},"canonical_sha256":"ece5f2f85da6892e7d745743987d23f6f18243f5def437a76c82469a91cb7216","source":{"kind":"arxiv","id":"1802.04944","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.04944","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"arxiv_version","alias_value":"1802.04944v2","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.04944","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"pith_short_12","alias_value":"5TS7F6C5U2ES","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"pith_short_16","alias_value":"5TS7F6C5U2ES47LU","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"pith_short_8","alias_value":"5TS7F6C5","created_at":"2026-07-05T04:01:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5TS7F6C5U2ES47LUK5BZQ7JD63","target":"record","payload":{"canonical_record":{"source":{"id":"1802.04944","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-14T03:52:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e266f77533cbd054f529a1699c55bba1f6ccfd1c270596749b8d9cc9c27ec83f","abstract_canon_sha256":"aca46a1a8af0da111012cb992c0d0f34ca5a4273d20ecb855e062c778bedbab0"},"schema_version":"1.0"},"canonical_sha256":"ece5f2f85da6892e7d745743987d23f6f18243f5def437a76c82469a91cb7216","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:01:20.372115Z","signature_b64":"KmxkWDtyO1I4GgKA330j2E2Oc9X1hbYGHjZI771rkiburyKNFVHio3vucXeilxvLFE/Wq3GT9ftJbvPY9kLRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ece5f2f85da6892e7d745743987d23f6f18243f5def437a76c82469a91cb7216","last_reissued_at":"2026-07-05T04:01:20.371709Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:01:20.371709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.04944","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":"2026-07-05T04:01:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PvbUWIun0OjuuzHgo60JnveL20VHXAnnkYZaxNBPrDFvoysQf3ZiY8KIAxZsq2A8DSbu/R7Q84dLpVXHkWVODQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:42:01.900899Z"},"content_sha256":"3d75c6ed642a3f721cdd77d82f4f391c000c6d5a48eee3c6da6970e29ee0b924","schema_version":"1.0","event_id":"sha256:3d75c6ed642a3f721cdd77d82f4f391c000c6d5a48eee3c6da6970e29ee0b924"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5TS7F6C5U2ES47LUK5BZQ7JD63","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Edge Attention-based Multi-Relational Graph Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chao Shang, Jiangwen Sun, Jinbo Bi, Jinfeng Yi, Jin Lu, Ko-Shin Chen, Qinqing Liu","submitted_at":"2018-02-14T03:52:58Z","abstract_excerpt":"Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary adjacency matrix is commonly used in training a GCN. Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We propose a new GCN model on the graphs where edges are characterized in multiple views or precisely in terms of multiple relationships. For instance, in chemical graph theory, compound structures are often represented by the hydrogen-depleted molecular graph where nodes correspond to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.04944","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/1802.04944/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":"2026-07-05T04:01:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zP89acajYTYNxlxuacUW4nVN5Bqa/xar5eJLgc2LKaU07UAL0pDE0tposLyLfWZti8hNwdRq/TlY/OkJXx8/BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:42:01.901833Z"},"content_sha256":"979b0864f086e9bcc9d5495cfe0daa1c6eb85414552d9fab3283913b5d15625c","schema_version":"1.0","event_id":"sha256:979b0864f086e9bcc9d5495cfe0daa1c6eb85414552d9fab3283913b5d15625c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5TS7F6C5U2ES47LUK5BZQ7JD63/bundle.json","state_url":"https://pith.science/pith/5TS7F6C5U2ES47LUK5BZQ7JD63/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5TS7F6C5U2ES47LUK5BZQ7JD63/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":"2026-08-08T19:42:01Z","links":{"resolver":"https://pith.science/pith/5TS7F6C5U2ES47LUK5BZQ7JD63","bundle":"https://pith.science/pith/5TS7F6C5U2ES47LUK5BZQ7JD63/bundle.json","state":"https://pith.science/pith/5TS7F6C5U2ES47LUK5BZQ7JD63/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5TS7F6C5U2ES47LUK5BZQ7JD63/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5TS7F6C5U2ES47LUK5BZQ7JD63","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":"aca46a1a8af0da111012cb992c0d0f34ca5a4273d20ecb855e062c778bedbab0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-14T03:52:58Z","title_canon_sha256":"e266f77533cbd054f529a1699c55bba1f6ccfd1c270596749b8d9cc9c27ec83f"},"schema_version":"1.0","source":{"id":"1802.04944","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.04944","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"arxiv_version","alias_value":"1802.04944v2","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.04944","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"pith_short_12","alias_value":"5TS7F6C5U2ES","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"pith_short_16","alias_value":"5TS7F6C5U2ES47LU","created_at":"2026-07-05T04:01:20Z"},{"alias_kind":"pith_short_8","alias_value":"5TS7F6C5","created_at":"2026-07-05T04:01:20Z"}],"graph_snapshots":[{"event_id":"sha256:979b0864f086e9bcc9d5495cfe0daa1c6eb85414552d9fab3283913b5d15625c","target":"graph","created_at":"2026-07-05T04:01:20Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1802.04944/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary adjacency matrix is commonly used in training a GCN. Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We propose a new GCN model on the graphs where edges are characterized in multiple views or precisely in terms of multiple relationships. For instance, in chemical graph theory, compound structures are often represented by the hydrogen-depleted molecular graph where nodes correspond to","authors_text":"Chao Shang, Jiangwen Sun, Jinbo Bi, Jinfeng Yi, Jin Lu, Ko-Shin Chen, Qinqing Liu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-14T03:52:58Z","title":"Edge Attention-based Multi-Relational Graph Convolutional Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.04944","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":"sha256:3d75c6ed642a3f721cdd77d82f4f391c000c6d5a48eee3c6da6970e29ee0b924","target":"record","created_at":"2026-07-05T04:01:20Z","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":"aca46a1a8af0da111012cb992c0d0f34ca5a4273d20ecb855e062c778bedbab0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-14T03:52:58Z","title_canon_sha256":"e266f77533cbd054f529a1699c55bba1f6ccfd1c270596749b8d9cc9c27ec83f"},"schema_version":"1.0","source":{"id":"1802.04944","kind":"arxiv","version":2}},"canonical_sha256":"ece5f2f85da6892e7d745743987d23f6f18243f5def437a76c82469a91cb7216","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ece5f2f85da6892e7d745743987d23f6f18243f5def437a76c82469a91cb7216","first_computed_at":"2026-07-05T04:01:20.371709Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:01:20.371709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KmxkWDtyO1I4GgKA330j2E2Oc9X1hbYGHjZI771rkiburyKNFVHio3vucXeilxvLFE/Wq3GT9ftJbvPY9kLRAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:01:20.372115Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.04944","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d75c6ed642a3f721cdd77d82f4f391c000c6d5a48eee3c6da6970e29ee0b924","sha256:979b0864f086e9bcc9d5495cfe0daa1c6eb85414552d9fab3283913b5d15625c"],"state_sha256":"fc9a3b08bf169d3bd03c66f545d2f96b6c373073720112c7d44ff8e2e251c6b1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zz1eHT2ylKJjdsr9ITgCRHj0D4ED3ByZqXSrsBhBfQHalp3B9RJLv7r0XJ0IViYHkwIKLFrx/VSoFM+M0oTWBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T19:42:01.908833Z","bundle_sha256":"1ac34c21971a89229ca8af04206c7ce5c2d609dff968f99627cf3cc1463fc322"}}