{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:NSNIWJY5L5UUW4BPTUS7SKRLRB","short_pith_number":"pith:NSNIWJY5","canonical_record":{"source":{"id":"2105.03821","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-09T03:25:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d5be94c430d7d2c7633897d7a1db546f825fb18b4e88e90fc1861aea91a011ae","abstract_canon_sha256":"dca0498e8af9ce27f71a9e9a84f33ef3ae0ca89cce14365b5e406e2160e4623c"},"schema_version":"1.0"},"canonical_sha256":"6c9a8b271d5f694b702f9d25f92a2b88447cad439c6793e898b57998e8e8abbe","source":{"kind":"arxiv","id":"2105.03821","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.03821","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"arxiv_version","alias_value":"2105.03821v2","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.03821","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"pith_short_12","alias_value":"NSNIWJY5L5UU","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"pith_short_16","alias_value":"NSNIWJY5L5UUW4BP","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"pith_short_8","alias_value":"NSNIWJY5","created_at":"2026-07-05T02:48:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:NSNIWJY5L5UUW4BPTUS7SKRLRB","target":"record","payload":{"canonical_record":{"source":{"id":"2105.03821","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-09T03:25:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d5be94c430d7d2c7633897d7a1db546f825fb18b4e88e90fc1861aea91a011ae","abstract_canon_sha256":"dca0498e8af9ce27f71a9e9a84f33ef3ae0ca89cce14365b5e406e2160e4623c"},"schema_version":"1.0"},"canonical_sha256":"6c9a8b271d5f694b702f9d25f92a2b88447cad439c6793e898b57998e8e8abbe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:48:37.462692Z","signature_b64":"AgUrAzoi4SfAm/clHvlU79KvMj03zopOcAtqWOSgNYwnVkv3o6jpssnnFI8Z9C8yGEQ7N/MFkFMC4ABGNvvkCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c9a8b271d5f694b702f9d25f92a2b88447cad439c6793e898b57998e8e8abbe","last_reissued_at":"2026-07-05T02:48:37.462212Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:48:37.462212Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.03821","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":"2026-07-05T02:48:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dxRmt3wvQVfzfot3P++batQK658fRYpuJE/nJZUhgGqFOE0Z003+bblExCNr39216HsqTHGaH+M37M+5muNiCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T02:31:49.384573Z"},"content_sha256":"2dcbe812b9753b0e20f780fcacb3de6045d1ce748b494c9b76523208dab5d38d","schema_version":"1.0","event_id":"sha256:2dcbe812b9753b0e20f780fcacb3de6045d1ce748b494c9b76523208dab5d38d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:NSNIWJY5L5UUW4BPTUS7SKRLRB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chuxiong Sun, Jie Hu, Jinpeng Chen, Yuheng Lu","submitted_at":"2021-05-09T03:25:58Z","abstract_excerpt":"Learning node representations that incorporate information from graph structure benefits wide range of tasks on graph. The majority of existing graph neural networks (GNNs) have limited power in capturing position information for a given node. The idea of positioning nodes with selected anchors has been exploited, yet mainly relying on explicit labeling of distance information. Here we propose Graph Inference Representation (GIR), an anchor based GNN model encoding path information related to pre-selected anchors for each node. Abilities to get position-aware embeddings are theoretically and e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.03821","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/2105.03821/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":"2026-07-05T02:48:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9O+X53HWkGC8bc+OVzbNMuFsqzypORzxom2eWpCuBcHoR4kUFq277eSpmx4cOk+rbkG+/cC8OqKPm5aHNPyEAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T02:31:49.384954Z"},"content_sha256":"e33662488db2d0c01fda417643037af9760cfa538b27a1f01408d3f00707aca9","schema_version":"1.0","event_id":"sha256:e33662488db2d0c01fda417643037af9760cfa538b27a1f01408d3f00707aca9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NSNIWJY5L5UUW4BPTUS7SKRLRB/bundle.json","state_url":"https://pith.science/pith/NSNIWJY5L5UUW4BPTUS7SKRLRB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NSNIWJY5L5UUW4BPTUS7SKRLRB/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":"2026-08-07T02:31:49Z","links":{"resolver":"https://pith.science/pith/NSNIWJY5L5UUW4BPTUS7SKRLRB","bundle":"https://pith.science/pith/NSNIWJY5L5UUW4BPTUS7SKRLRB/bundle.json","state":"https://pith.science/pith/NSNIWJY5L5UUW4BPTUS7SKRLRB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NSNIWJY5L5UUW4BPTUS7SKRLRB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:NSNIWJY5L5UUW4BPTUS7SKRLRB","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":"dca0498e8af9ce27f71a9e9a84f33ef3ae0ca89cce14365b5e406e2160e4623c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-09T03:25:58Z","title_canon_sha256":"d5be94c430d7d2c7633897d7a1db546f825fb18b4e88e90fc1861aea91a011ae"},"schema_version":"1.0","source":{"id":"2105.03821","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.03821","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"arxiv_version","alias_value":"2105.03821v2","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.03821","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"pith_short_12","alias_value":"NSNIWJY5L5UU","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"pith_short_16","alias_value":"NSNIWJY5L5UUW4BP","created_at":"2026-07-05T02:48:37Z"},{"alias_kind":"pith_short_8","alias_value":"NSNIWJY5","created_at":"2026-07-05T02:48:37Z"}],"graph_snapshots":[{"event_id":"sha256:e33662488db2d0c01fda417643037af9760cfa538b27a1f01408d3f00707aca9","target":"graph","created_at":"2026-07-05T02:48:37Z","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/2105.03821/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning node representations that incorporate information from graph structure benefits wide range of tasks on graph. The majority of existing graph neural networks (GNNs) have limited power in capturing position information for a given node. The idea of positioning nodes with selected anchors has been exploited, yet mainly relying on explicit labeling of distance information. Here we propose Graph Inference Representation (GIR), an anchor based GNN model encoding path information related to pre-selected anchors for each node. Abilities to get position-aware embeddings are theoretically and e","authors_text":"Chuxiong Sun, Jie Hu, Jinpeng Chen, Yuheng Lu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-09T03:25:58Z","title":"Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.03821","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":"sha256:2dcbe812b9753b0e20f780fcacb3de6045d1ce748b494c9b76523208dab5d38d","target":"record","created_at":"2026-07-05T02:48:37Z","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":"dca0498e8af9ce27f71a9e9a84f33ef3ae0ca89cce14365b5e406e2160e4623c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-09T03:25:58Z","title_canon_sha256":"d5be94c430d7d2c7633897d7a1db546f825fb18b4e88e90fc1861aea91a011ae"},"schema_version":"1.0","source":{"id":"2105.03821","kind":"arxiv","version":2}},"canonical_sha256":"6c9a8b271d5f694b702f9d25f92a2b88447cad439c6793e898b57998e8e8abbe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c9a8b271d5f694b702f9d25f92a2b88447cad439c6793e898b57998e8e8abbe","first_computed_at":"2026-07-05T02:48:37.462212Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:48:37.462212Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AgUrAzoi4SfAm/clHvlU79KvMj03zopOcAtqWOSgNYwnVkv3o6jpssnnFI8Z9C8yGEQ7N/MFkFMC4ABGNvvkCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:48:37.462692Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.03821","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2dcbe812b9753b0e20f780fcacb3de6045d1ce748b494c9b76523208dab5d38d","sha256:e33662488db2d0c01fda417643037af9760cfa538b27a1f01408d3f00707aca9"],"state_sha256":"d00fa23d37975f7a79af6ea5080fc3af59c57bd53b63afbcd0f507c1305dacaa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ATHQ9Ro2e2rliKt/uTf1WTpT8WbSgvv9qmXchevLndotY6/bGNtU9gTsaJllNlWDA388tDXZfl7NZEEs40yWAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T02:31:49.387990Z","bundle_sha256":"96a03284ce9099eca28935902b2e464355626129893e6c1dab49468fb905822e"}}