{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ZRNS6RBY4FFH6WRIICOYFEGZFL","short_pith_number":"pith:ZRNS6RBY","canonical_record":{"source":{"id":"1711.05697","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-15T17:48:35Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"b8fbcf5604b62482b1e61817074e5cbd7273a40c3a65c8585996c991eb754921","abstract_canon_sha256":"ee15537dc814b6750507bbb55a18cbb5397c4d73d22139de0e8e791c47fead37"},"schema_version":"1.0"},"canonical_sha256":"cc5b2f4438e14a7f5a28409d8290d92afb2dba80b249b75db09c5d70032d1a26","source":{"kind":"arxiv","id":"1711.05697","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05697","created_at":"2026-05-17T23:40:04Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05697v4","created_at":"2026-05-17T23:40:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05697","created_at":"2026-05-17T23:40:04Z"},{"alias_kind":"pith_short_12","alias_value":"ZRNS6RBY4FFH","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZRNS6RBY4FFH6WRI","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZRNS6RBY","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ZRNS6RBY4FFH6WRIICOYFEGZFL","target":"record","payload":{"canonical_record":{"source":{"id":"1711.05697","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-15T17:48:35Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"b8fbcf5604b62482b1e61817074e5cbd7273a40c3a65c8585996c991eb754921","abstract_canon_sha256":"ee15537dc814b6750507bbb55a18cbb5397c4d73d22139de0e8e791c47fead37"},"schema_version":"1.0"},"canonical_sha256":"cc5b2f4438e14a7f5a28409d8290d92afb2dba80b249b75db09c5d70032d1a26","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:04.703724Z","signature_b64":"iA5M7GZkGHKhaXilk/vrBA1MaiWPhu2JnR7zVCo4WaSpCKHh7DtGQbYw+oTBI9RoPDXgcDvFb/Y2La7IKO49Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc5b2f4438e14a7f5a28409d8290d92afb2dba80b249b75db09c5d70032d1a26","last_reissued_at":"2026-05-17T23:40:04.703105Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:04.703105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.05697","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:40:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aHW+slLr0xMxcqtQwj4oarctPV5uniSK/BqDfJ7kTY44GBHkr16+3ZYBrBoq5larA5bz3sIyhh1zMGtdIsa2Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:11:52.577393Z"},"content_sha256":"e06a3579a21b85f171370c950284d0d468f5451670760780561fdfbfff0f823d","schema_version":"1.0","event_id":"sha256:e06a3579a21b85f171370c950284d0d468f5451670760780561fdfbfff0f823d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ZRNS6RBY4FFH6WRIICOYFEGZFL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Motif-based Convolutional Neural Network on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.LG","authors_text":"Aravind Sankar, Kevin Chen-Chuan Chang, Xinyang Zhang","submitted_at":"2017-11-15T17:48:35Z","abstract_excerpt":"This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas. We propose a novel spatial convolution operation to model the key properties of local connectivity and translation invariance, using high-order connection patterns or motifs. We develop a novel deep architecture Motif-CNN that employs an attention model to combine the features extracted from multiple patterns, thus effectively capturing high-order structural and feature information. Our experiments on semi-supe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05697","kind":"arxiv","version":4},"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":"2026-05-17T23:40:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bSGs5wSRx+XCzdBff3QnGRY3Q9hiKkl2QvvWNoFqBblFju1vW/Rjg/w4yWyj2tElTm86mtOHqBHICcJVf9nXDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:11:52.578138Z"},"content_sha256":"3b86d720fdec244678891acb634512b89f53facc1c98bc967d82f1fa5e85fe91","schema_version":"1.0","event_id":"sha256:3b86d720fdec244678891acb634512b89f53facc1c98bc967d82f1fa5e85fe91"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZRNS6RBY4FFH6WRIICOYFEGZFL/bundle.json","state_url":"https://pith.science/pith/ZRNS6RBY4FFH6WRIICOYFEGZFL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZRNS6RBY4FFH6WRIICOYFEGZFL/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":"2026-05-25T23:11:52Z","links":{"resolver":"https://pith.science/pith/ZRNS6RBY4FFH6WRIICOYFEGZFL","bundle":"https://pith.science/pith/ZRNS6RBY4FFH6WRIICOYFEGZFL/bundle.json","state":"https://pith.science/pith/ZRNS6RBY4FFH6WRIICOYFEGZFL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZRNS6RBY4FFH6WRIICOYFEGZFL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZRNS6RBY4FFH6WRIICOYFEGZFL","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":"ee15537dc814b6750507bbb55a18cbb5397c4d73d22139de0e8e791c47fead37","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-15T17:48:35Z","title_canon_sha256":"b8fbcf5604b62482b1e61817074e5cbd7273a40c3a65c8585996c991eb754921"},"schema_version":"1.0","source":{"id":"1711.05697","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05697","created_at":"2026-05-17T23:40:04Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05697v4","created_at":"2026-05-17T23:40:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05697","created_at":"2026-05-17T23:40:04Z"},{"alias_kind":"pith_short_12","alias_value":"ZRNS6RBY4FFH","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZRNS6RBY4FFH6WRI","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZRNS6RBY","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:3b86d720fdec244678891acb634512b89f53facc1c98bc967d82f1fa5e85fe91","target":"graph","created_at":"2026-05-17T23:40:04Z","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 introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas. We propose a novel spatial convolution operation to model the key properties of local connectivity and translation invariance, using high-order connection patterns or motifs. We develop a novel deep architecture Motif-CNN that employs an attention model to combine the features extracted from multiple patterns, thus effectively capturing high-order structural and feature information. Our experiments on semi-supe","authors_text":"Aravind Sankar, Kevin Chen-Chuan Chang, Xinyang Zhang","cross_cats":["cs.SI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-15T17:48:35Z","title":"Motif-based Convolutional Neural Network on Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05697","kind":"arxiv","version":4},"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:e06a3579a21b85f171370c950284d0d468f5451670760780561fdfbfff0f823d","target":"record","created_at":"2026-05-17T23:40:04Z","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":"ee15537dc814b6750507bbb55a18cbb5397c4d73d22139de0e8e791c47fead37","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-15T17:48:35Z","title_canon_sha256":"b8fbcf5604b62482b1e61817074e5cbd7273a40c3a65c8585996c991eb754921"},"schema_version":"1.0","source":{"id":"1711.05697","kind":"arxiv","version":4}},"canonical_sha256":"cc5b2f4438e14a7f5a28409d8290d92afb2dba80b249b75db09c5d70032d1a26","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc5b2f4438e14a7f5a28409d8290d92afb2dba80b249b75db09c5d70032d1a26","first_computed_at":"2026-05-17T23:40:04.703105Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:04.703105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iA5M7GZkGHKhaXilk/vrBA1MaiWPhu2JnR7zVCo4WaSpCKHh7DtGQbYw+oTBI9RoPDXgcDvFb/Y2La7IKO49Dg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:04.703724Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.05697","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e06a3579a21b85f171370c950284d0d468f5451670760780561fdfbfff0f823d","sha256:3b86d720fdec244678891acb634512b89f53facc1c98bc967d82f1fa5e85fe91"],"state_sha256":"0c555490e73018e90e416a2ec24ab8571a11fef13c726a72215299f41f5f0cff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pBOlpXf0Af7MuUbceC22DhYddlk1hehZlzd/v+9zhrOUYXS9fD4rY0FpVJyOPSsXdDbqOPl7zhF0nZAC18M8AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T23:11:52.581980Z","bundle_sha256":"4c2cfec9248199635347f852f56c92ec5d667353ff720e18af07983ff209e8f7"}}