{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CITDLNKPQ2AGC4EP6VKX2UN6MI","short_pith_number":"pith:CITDLNKP","schema_version":"1.0","canonical_sha256":"122635b54f868061708ff5557d51be62322c4d5d925754ece6c6af7dda1d2231","source":{"kind":"arxiv","id":"2311.08798","version":1},"attestation_state":"computed","paper":{"title":"X-GRL: An Empirical Assessment of Explainable GNN-DRL in B5G/6G Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Engin Zeydan, Farhad Rezazadeh, Houbing Song, Josep Mangues-Bafalluy, K.P. Subbalakshmi, Sergio Barrachina-Munoz","submitted_at":"2023-11-15T09:11:37Z","abstract_excerpt":"The rapid development of artificial intelligence (AI) techniques has triggered a revolution in beyond fifth-generation (B5G) and upcoming sixth-generation (6G) mobile networks. Despite these advances, efficient resource allocation in dynamic and complex networks remains a major challenge. This paper presents an experimental implementation of deep reinforcement learning (DRL) enhanced with graph neural networks (GNNs) on a real 5G testbed. The method addresses the explainability of GNNs by evaluating the importance of each edge in determining the model's output. The custom sampling functions fe"},"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":"2311.08798","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2023-11-15T09:11:37Z","cross_cats_sorted":[],"title_canon_sha256":"b7be1b223ff8039ef2f069912ce5a5bd1aab71ce682387effadaa5bbd4d5be5b","abstract_canon_sha256":"164d9ae9f7a406c58a74987ba932325a9e54b6390979f4c377b1d6220957efc0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:13:03.473830Z","signature_b64":"QfYrvg3g++FXhm/uwE/14rPvIxrB9y9wmOqb6QGUt0i3cMq8rzloZV6el5dnXhF0J6fqhio+kXldsRrPixPhDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"122635b54f868061708ff5557d51be62322c4d5d925754ece6c6af7dda1d2231","last_reissued_at":"2026-07-05T07:13:03.473384Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:13:03.473384Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"X-GRL: An Empirical Assessment of Explainable GNN-DRL in B5G/6G Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Engin Zeydan, Farhad Rezazadeh, Houbing Song, Josep Mangues-Bafalluy, K.P. Subbalakshmi, Sergio Barrachina-Munoz","submitted_at":"2023-11-15T09:11:37Z","abstract_excerpt":"The rapid development of artificial intelligence (AI) techniques has triggered a revolution in beyond fifth-generation (B5G) and upcoming sixth-generation (6G) mobile networks. Despite these advances, efficient resource allocation in dynamic and complex networks remains a major challenge. This paper presents an experimental implementation of deep reinforcement learning (DRL) enhanced with graph neural networks (GNNs) on a real 5G testbed. The method addresses the explainability of GNNs by evaluating the importance of each edge in determining the model's output. The custom sampling functions fe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08798","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/2311.08798/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":"2311.08798","created_at":"2026-07-05T07:13:03.473453+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.08798v1","created_at":"2026-07-05T07:13:03.473453+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08798","created_at":"2026-07-05T07:13:03.473453+00:00"},{"alias_kind":"pith_short_12","alias_value":"CITDLNKPQ2AG","created_at":"2026-07-05T07:13:03.473453+00:00"},{"alias_kind":"pith_short_16","alias_value":"CITDLNKPQ2AGC4EP","created_at":"2026-07-05T07:13:03.473453+00:00"},{"alias_kind":"pith_short_8","alias_value":"CITDLNKP","created_at":"2026-07-05T07:13:03.473453+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/CITDLNKPQ2AGC4EP6VKX2UN6MI","json":"https://pith.science/pith/CITDLNKPQ2AGC4EP6VKX2UN6MI.json","graph_json":"https://pith.science/api/pith-number/CITDLNKPQ2AGC4EP6VKX2UN6MI/graph.json","events_json":"https://pith.science/api/pith-number/CITDLNKPQ2AGC4EP6VKX2UN6MI/events.json","paper":"https://pith.science/paper/CITDLNKP"},"agent_actions":{"view_html":"https://pith.science/pith/CITDLNKPQ2AGC4EP6VKX2UN6MI","download_json":"https://pith.science/pith/CITDLNKPQ2AGC4EP6VKX2UN6MI.json","view_paper":"https://pith.science/paper/CITDLNKP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.08798&json=true","fetch_graph":"https://pith.science/api/pith-number/CITDLNKPQ2AGC4EP6VKX2UN6MI/graph.json","fetch_events":"https://pith.science/api/pith-number/CITDLNKPQ2AGC4EP6VKX2UN6MI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CITDLNKPQ2AGC4EP6VKX2UN6MI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CITDLNKPQ2AGC4EP6VKX2UN6MI/action/storage_attestation","attest_author":"https://pith.science/pith/CITDLNKPQ2AGC4EP6VKX2UN6MI/action/author_attestation","sign_citation":"https://pith.science/pith/CITDLNKPQ2AGC4EP6VKX2UN6MI/action/citation_signature","submit_replication":"https://pith.science/pith/CITDLNKPQ2AGC4EP6VKX2UN6MI/action/replication_record"}},"created_at":"2026-07-05T07:13:03.473453+00:00","updated_at":"2026-07-05T07:13:03.473453+00:00"}