{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:A54OIRCQ6CUQCVSRMVNTPQD32V","short_pith_number":"pith:A54OIRCQ","schema_version":"1.0","canonical_sha256":"0778e44450f0a9015651655b37c07bd56ed6c427d0e253805e8c6a0b3295d96c","source":{"kind":"arxiv","id":"2209.15240","version":5},"attestation_state":"computed","paper":{"title":"Universal Prompt Tuning for Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chunping Wang, Lei Chen, Taoran Fang, Yang Yang, Yunchao Zhang","submitted_at":"2022-09-30T05:19:27Z","abstract_excerpt":"In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike the unified pre-training strategy employed in the language field, the graph field exhibits diverse pre-training strategies, posing challenges in designing appropriate prompt-based tuning methods for graph neural networks. While some pioneering work has devised specialized prompting functions for models that employ edge prediction as their pre-training tasks, these methods are limited to specific pre-trained GNN models and lack broader applicability. In this paper, we introduce a universal prompt-"},"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":"2209.15240","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-30T05:19:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e3fa81d9375bba3f13e20ddcdcd6665a4c6633300c5345438618205a5c26a301","abstract_canon_sha256":"bbdb7c04fddbd928cd6e2be11e411e47a8b4e14fab81f955857e6ae6b8efcf94"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:06:22.470763Z","signature_b64":"xg4VwXIwsj3Tj9S9rru6TboYiMfm8mZgJJ+up2k+l9GO+xLpaFYoDuS884e1iY7V2ahxz5EFVpwSGagb2TXyBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0778e44450f0a9015651655b37c07bd56ed6c427d0e253805e8c6a0b3295d96c","last_reissued_at":"2026-07-05T08:06:22.470233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:06:22.470233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Universal Prompt Tuning for Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chunping Wang, Lei Chen, Taoran Fang, Yang Yang, Yunchao Zhang","submitted_at":"2022-09-30T05:19:27Z","abstract_excerpt":"In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike the unified pre-training strategy employed in the language field, the graph field exhibits diverse pre-training strategies, posing challenges in designing appropriate prompt-based tuning methods for graph neural networks. While some pioneering work has devised specialized prompting functions for models that employ edge prediction as their pre-training tasks, these methods are limited to specific pre-trained GNN models and lack broader applicability. In this paper, we introduce a universal prompt-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.15240","kind":"arxiv","version":5},"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/2209.15240/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":"2209.15240","created_at":"2026-07-05T08:06:22.470297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.15240v5","created_at":"2026-07-05T08:06:22.470297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.15240","created_at":"2026-07-05T08:06:22.470297+00:00"},{"alias_kind":"pith_short_12","alias_value":"A54OIRCQ6CUQ","created_at":"2026-07-05T08:06:22.470297+00:00"},{"alias_kind":"pith_short_16","alias_value":"A54OIRCQ6CUQCVSR","created_at":"2026-07-05T08:06:22.470297+00:00"},{"alias_kind":"pith_short_8","alias_value":"A54OIRCQ","created_at":"2026-07-05T08:06:22.470297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.08273","citing_title":"Efficient Prompt Learning for Traffic Forecasting","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V","json":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V.json","graph_json":"https://pith.science/api/pith-number/A54OIRCQ6CUQCVSRMVNTPQD32V/graph.json","events_json":"https://pith.science/api/pith-number/A54OIRCQ6CUQCVSRMVNTPQD32V/events.json","paper":"https://pith.science/paper/A54OIRCQ"},"agent_actions":{"view_html":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V","download_json":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V.json","view_paper":"https://pith.science/paper/A54OIRCQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.15240&json=true","fetch_graph":"https://pith.science/api/pith-number/A54OIRCQ6CUQCVSRMVNTPQD32V/graph.json","fetch_events":"https://pith.science/api/pith-number/A54OIRCQ6CUQCVSRMVNTPQD32V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/action/storage_attestation","attest_author":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/action/author_attestation","sign_citation":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/action/citation_signature","submit_replication":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/action/replication_record"}},"created_at":"2026-07-05T08:06:22.470297+00:00","updated_at":"2026-07-05T08:06:22.470297+00:00"}