{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QQLBYKIGOPWEQPIVYCUD4MRFNQ","short_pith_number":"pith:QQLBYKIG","schema_version":"1.0","canonical_sha256":"84161c290673ec483d15c0a83e32256c1f8262a34c5fe9eb7879d834df11dfae","source":{"kind":"arxiv","id":"2412.07189","version":1},"attestation_state":"computed","paper":{"title":"When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Dusit Niyato, Ruichen Zhang, Shiwen Mao, Yang Xiong, Ying-Chang Liang, Yinqiu Liu, Zehui Xiong","submitted_at":"2024-12-10T04:55:57Z","abstract_excerpt":"The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into"},"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":"2412.07189","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2024-12-10T04:55:57Z","cross_cats_sorted":[],"title_canon_sha256":"afd7d3609c73cd153f31d475453b703215f68691abe4b5312d3407952db123f5","abstract_canon_sha256":"2f4994f12e8a70fbe60b52491abdf2b57564aafecb5861c091af5beb619055fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:05.661151Z","signature_b64":"21gB46LfSToUG9d6VKiseQSHPvw5hI99SkQ9wXv6rPxgsVARNPFx8Gw1kZyrIlQKDxfqz19fEVBty4s3AZRXDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84161c290673ec483d15c0a83e32256c1f8262a34c5fe9eb7879d834df11dfae","last_reissued_at":"2026-07-05T09:47:05.660657Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:05.660657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Dusit Niyato, Ruichen Zhang, Shiwen Mao, Yang Xiong, Ying-Chang Liang, Yinqiu Liu, Zehui Xiong","submitted_at":"2024-12-10T04:55:57Z","abstract_excerpt":"The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.07189","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.07189/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2412.07189","created_at":"2026-07-05T09:47:05.660711+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.07189v1","created_at":"2026-07-05T09:47:05.660711+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.07189","created_at":"2026-07-05T09:47:05.660711+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQLBYKIGOPWE","created_at":"2026-07-05T09:47:05.660711+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQLBYKIGOPWEQPIV","created_at":"2026-07-05T09:47:05.660711+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQLBYKIG","created_at":"2026-07-05T09:47:05.660711+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03608","citing_title":"Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ","json":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ.json","graph_json":"https://pith.science/api/pith-number/QQLBYKIGOPWEQPIVYCUD4MRFNQ/graph.json","events_json":"https://pith.science/api/pith-number/QQLBYKIGOPWEQPIVYCUD4MRFNQ/events.json","paper":"https://pith.science/paper/QQLBYKIG"},"agent_actions":{"view_html":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ","download_json":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ.json","view_paper":"https://pith.science/paper/QQLBYKIG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.07189&json=true","fetch_graph":"https://pith.science/api/pith-number/QQLBYKIGOPWEQPIVYCUD4MRFNQ/graph.json","fetch_events":"https://pith.science/api/pith-number/QQLBYKIGOPWEQPIVYCUD4MRFNQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ/action/storage_attestation","attest_author":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ/action/author_attestation","sign_citation":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ/action/citation_signature","submit_replication":"https://pith.science/pith/QQLBYKIGOPWEQPIVYCUD4MRFNQ/action/replication_record"}},"created_at":"2026-07-05T09:47:05.660711+00:00","updated_at":"2026-07-05T09:47:05.660711+00:00"}