{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:C6HJV6FMQMJ7WWQWESRJS4M775","short_pith_number":"pith:C6HJV6FM","schema_version":"1.0","canonical_sha256":"178e9af8ac8313fb5a1624a299719fff4450c76a3082679624808a4abd6676a6","source":{"kind":"arxiv","id":"1809.02482","version":1},"attestation_state":"computed","paper":{"title":"BiasedWalk: Biased Sampling for Representation Learning on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Duong Nguyen, Fragkiskos D. Malliaros","submitted_at":"2018-09-07T13:58:37Z","abstract_excerpt":"Network embedding algorithms are able to learn latent feature representations of nodes, transforming networks into lower dimensional vector representations. Typical key applications, which have effectively been addressed using network embeddings, include link prediction, multilabel classification and community detection. In this paper, we propose BiasedWalk, a scalable, unsupervised feature learning algorithm that is based on biased random walks to sample context information about each node in the network. Our random-walk based sampling can behave as Breath-First-Search (BFS) and Depth-First-S"},"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":"1809.02482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-07T13:58:37Z","cross_cats_sorted":["cs.SI","stat.ML"],"title_canon_sha256":"87a62f233aabbb23afeac7510cdfaa2b00bcbec7c0128150c6c8717c2ac93329","abstract_canon_sha256":"0a8b92041872aea95bfd0bcf2206a7c4ad002a747b197902aedb2abd1e3d3ea8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:16.992696Z","signature_b64":"A+WTLZlGp1twrY/b7miE1F5jRBh/tkEwBmpj+SyhgHUJtI7dU0CkSZERmYo8Gp9gCIxO0uCxkBHUF06YuF2JBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"178e9af8ac8313fb5a1624a299719fff4450c76a3082679624808a4abd6676a6","last_reissued_at":"2026-05-18T00:06:16.992227Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:16.992227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BiasedWalk: Biased Sampling for Representation Learning on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Duong Nguyen, Fragkiskos D. Malliaros","submitted_at":"2018-09-07T13:58:37Z","abstract_excerpt":"Network embedding algorithms are able to learn latent feature representations of nodes, transforming networks into lower dimensional vector representations. Typical key applications, which have effectively been addressed using network embeddings, include link prediction, multilabel classification and community detection. In this paper, we propose BiasedWalk, a scalable, unsupervised feature learning algorithm that is based on biased random walks to sample context information about each node in the network. Our random-walk based sampling can behave as Breath-First-Search (BFS) and Depth-First-S"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02482","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":"1809.02482","created_at":"2026-05-18T00:06:16.992312+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.02482v1","created_at":"2026-05-18T00:06:16.992312+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02482","created_at":"2026-05-18T00:06:16.992312+00:00"},{"alias_kind":"pith_short_12","alias_value":"C6HJV6FMQMJ7","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"C6HJV6FMQMJ7WWQW","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"C6HJV6FM","created_at":"2026-05-18T12:32:16.446611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775","json":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775.json","graph_json":"https://pith.science/api/pith-number/C6HJV6FMQMJ7WWQWESRJS4M775/graph.json","events_json":"https://pith.science/api/pith-number/C6HJV6FMQMJ7WWQWESRJS4M775/events.json","paper":"https://pith.science/paper/C6HJV6FM"},"agent_actions":{"view_html":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775","download_json":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775.json","view_paper":"https://pith.science/paper/C6HJV6FM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.02482&json=true","fetch_graph":"https://pith.science/api/pith-number/C6HJV6FMQMJ7WWQWESRJS4M775/graph.json","fetch_events":"https://pith.science/api/pith-number/C6HJV6FMQMJ7WWQWESRJS4M775/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775/action/storage_attestation","attest_author":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775/action/author_attestation","sign_citation":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775/action/citation_signature","submit_replication":"https://pith.science/pith/C6HJV6FMQMJ7WWQWESRJS4M775/action/replication_record"}},"created_at":"2026-05-18T00:06:16.992312+00:00","updated_at":"2026-05-18T00:06:16.992312+00:00"}