{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:TTHME2LWEH7KSNQYNYXPIEFMYW","short_pith_number":"pith:TTHME2LW","schema_version":"1.0","canonical_sha256":"9ccec2697621fea936186e2ef410acc58b6e6d896c0331a3362a4b36ff0f352d","source":{"kind":"arxiv","id":"1707.05005","version":1},"attestation_state":"computed","paper":{"title":"graph2vec: Learning Distributed Representations of Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CR","cs.NE","cs.SE"],"primary_cat":"cs.AI","authors_text":"Annamalai Narayanan, Lihui Chen, Mahinthan Chandramohan, Rajasekar Venkatesan, Shantanu Jaiswal, Yang Liu","submitted_at":"2017-07-17T05:09:03Z","abstract_excerpt":"Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed length feature vectors. While the aforementioned approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are hampered"},"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":"1707.05005","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-07-17T05:09:03Z","cross_cats_sorted":["cs.CL","cs.CR","cs.NE","cs.SE"],"title_canon_sha256":"89623caba74ad8bd91c1ebdf325fc7d9c738a2115f73fa50fdf6c477deff7d97","abstract_canon_sha256":"eb5e0c695a94f34a238c53ddef747c1ea432e0f6b724dd6b27e8bbd2a5f958af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:11.624243Z","signature_b64":"0ekYY3vkx5i9yvgBEiskddCVkF1cze3fOPvR+FXppmbVRNf9Q+ETzWIpbtYqpjavlkDXNNaTdLfYbbY8OeNpBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ccec2697621fea936186e2ef410acc58b6e6d896c0331a3362a4b36ff0f352d","last_reissued_at":"2026-05-18T00:40:11.623684Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:11.623684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"graph2vec: Learning Distributed Representations of Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CR","cs.NE","cs.SE"],"primary_cat":"cs.AI","authors_text":"Annamalai Narayanan, Lihui Chen, Mahinthan Chandramohan, Rajasekar Venkatesan, Shantanu Jaiswal, Yang Liu","submitted_at":"2017-07-17T05:09:03Z","abstract_excerpt":"Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed length feature vectors. While the aforementioned approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are hampered"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.05005","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1707.05005","created_at":"2026-05-18T00:40:11.623764+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.05005v1","created_at":"2026-05-18T00:40:11.623764+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.05005","created_at":"2026-05-18T00:40:11.623764+00:00"},{"alias_kind":"pith_short_12","alias_value":"TTHME2LWEH7K","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"TTHME2LWEH7KSNQY","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"TTHME2LW","created_at":"2026-05-18T12:31:46.661854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2402.02953","citing_title":"Unraveling the Key of Machine Learning-based Android Malware Detection","ref_index":82,"is_internal_anchor":true},{"citing_arxiv_id":"2408.13471","citing_title":"Disentangled Generative Graph Representation Learning","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2501.07557","citing_title":"Decoding Musical Evolution Through Network Science","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2604.25275","citing_title":"Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06466","citing_title":"Diversity Curves for Graph Representation Learning","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16509","citing_title":"Learning-Based Sparsification of Dynamic Graphs in Robotic Exploration Algorithms","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW","json":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW.json","graph_json":"https://pith.science/api/pith-number/TTHME2LWEH7KSNQYNYXPIEFMYW/graph.json","events_json":"https://pith.science/api/pith-number/TTHME2LWEH7KSNQYNYXPIEFMYW/events.json","paper":"https://pith.science/paper/TTHME2LW"},"agent_actions":{"view_html":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW","download_json":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW.json","view_paper":"https://pith.science/paper/TTHME2LW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.05005&json=true","fetch_graph":"https://pith.science/api/pith-number/TTHME2LWEH7KSNQYNYXPIEFMYW/graph.json","fetch_events":"https://pith.science/api/pith-number/TTHME2LWEH7KSNQYNYXPIEFMYW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW/action/storage_attestation","attest_author":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW/action/author_attestation","sign_citation":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW/action/citation_signature","submit_replication":"https://pith.science/pith/TTHME2LWEH7KSNQYNYXPIEFMYW/action/replication_record"}},"created_at":"2026-05-18T00:40:11.623764+00:00","updated_at":"2026-05-18T00:40:11.623764+00:00"}