{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:PVFHEBSTBWTTWZSS6HT6657CW7","short_pith_number":"pith:PVFHEBST","schema_version":"1.0","canonical_sha256":"7d4a7206530da73b6652f1e7ef77e2b7c8cc124a4a5681f41b773291feb9c022","source":{"kind":"arxiv","id":"1709.05584","version":3},"attestation_state":"computed","paper":{"title":"Representation Learning on Graphs: Methods and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Jure Leskovec, Rex Ying, William L. Hamilton","submitted_at":"2017-09-17T00:19:33Z","abstract_excerpt":"Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph st"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1709.05584","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2017-09-17T00:19:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"66f3badd8965ecfc7aaa3844c1b74aa6f7afcf756989850aa318d791815e0f7a","abstract_canon_sha256":"7533e49719bcd4f8ded234ccc972811a4f535b7ceee21de50802aef2d02ee24f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:53.353666Z","signature_b64":"sXYGwnj6WtQJehmLHbyPr+nMd3Rl5l8HGwZd3LqZeN4thfuP3rVnV6+t5tdJ8aB+pVPTl1DSJwytv2g5X7z4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d4a7206530da73b6652f1e7ef77e2b7c8cc124a4a5681f41b773291feb9c022","last_reissued_at":"2026-05-18T00:18:53.353167Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:53.353167Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Representation Learning on Graphs: Methods and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Jure Leskovec, Rex Ying, William L. Hamilton","submitted_at":"2017-09-17T00:19:33Z","abstract_excerpt":"Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.05584","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1709.05584","created_at":"2026-05-18T00:18:53.353238+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.05584v3","created_at":"2026-05-18T00:18:53.353238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.05584","created_at":"2026-05-18T00:18:53.353238+00:00"},{"alias_kind":"pith_short_12","alias_value":"PVFHEBSTBWTT","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"PVFHEBSTBWTTWZSS","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"PVFHEBST","created_at":"2026-05-18T12:31:37.085036+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":5,"sample":[{"citing_arxiv_id":"1907.00710","citing_title":"Deep Conversational Recommender in Travel","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"1907.02222","citing_title":"Tracking Temporal Evolution of Graphs using Non-Timestamped Data","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"1907.02811","citing_title":"Network Embedding: on Compression and Learning","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"1907.09000","citing_title":"Image Classification with Hierarchical Multigraph Networks","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"2006.10637","citing_title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","ref_index":111,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07148","citing_title":"Uncovering and Shaping the Latent Representation of 3D Scene Topology in Vision-Language Models","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7","json":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7.json","graph_json":"https://pith.science/api/pith-number/PVFHEBSTBWTTWZSS6HT6657CW7/graph.json","events_json":"https://pith.science/api/pith-number/PVFHEBSTBWTTWZSS6HT6657CW7/events.json","paper":"https://pith.science/paper/PVFHEBST"},"agent_actions":{"view_html":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7","download_json":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7.json","view_paper":"https://pith.science/paper/PVFHEBST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.05584&json=true","fetch_graph":"https://pith.science/api/pith-number/PVFHEBSTBWTTWZSS6HT6657CW7/graph.json","fetch_events":"https://pith.science/api/pith-number/PVFHEBSTBWTTWZSS6HT6657CW7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7/action/storage_attestation","attest_author":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7/action/author_attestation","sign_citation":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7/action/citation_signature","submit_replication":"https://pith.science/pith/PVFHEBSTBWTTWZSS6HT6657CW7/action/replication_record"}},"created_at":"2026-05-18T00:18:53.353238+00:00","updated_at":"2026-05-18T00:18:53.353238+00:00"}