{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QFMNEBYMSZTHHVDMHODJFPMTSQ","short_pith_number":"pith:QFMNEBYM","schema_version":"1.0","canonical_sha256":"8158d2070c966673d46c3b8692bd939433cc60654a5b11ad8d7d8d0b6b332762","source":{"kind":"arxiv","id":"2403.20221","version":1},"attestation_state":"computed","paper":{"title":"Graph Neural Aggregation-diffusion with Metastability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Kaiyuan Cui, Weichen Zhao, Xinyan Wang, Zicheng Zhang","submitted_at":"2024-03-29T15:05:57Z","abstract_excerpt":"Continuous graph neural models based on differential equations have expanded the architecture of graph neural networks (GNNs). Due to the connection between graph diffusion and message passing, diffusion-based models have been widely studied. However, diffusion naturally drives the system towards an equilibrium state, leading to issues like over-smoothing. To this end, we propose GRADE inspired by graph aggregation-diffusion equations, which includes the delicate balance between nonlinear diffusion and aggregation induced by interaction potentials. The node representations obtained through agg"},"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":"2403.20221","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-29T15:05:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ffe922674b7c5df4e88ea6fa7f8ebceb10b3151cc8bfdb44dc51067e691bd3ed","abstract_canon_sha256":"951a81df9d257acaba8b253be7359d0ac4ea43567c19cc5a854dac53ef45f7e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:15.900893Z","signature_b64":"w59CHmD+PFOKloQUqqSeCusROl6AEI+nkimixwD8pAtKMbgnA1rq21dLZpdxK4c0Ie5MPV0cUajoXeQkEuhBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8158d2070c966673d46c3b8692bd939433cc60654a5b11ad8d7d8d0b6b332762","last_reissued_at":"2026-07-05T08:02:15.900394Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:15.900394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Neural Aggregation-diffusion with Metastability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Kaiyuan Cui, Weichen Zhao, Xinyan Wang, Zicheng Zhang","submitted_at":"2024-03-29T15:05:57Z","abstract_excerpt":"Continuous graph neural models based on differential equations have expanded the architecture of graph neural networks (GNNs). Due to the connection between graph diffusion and message passing, diffusion-based models have been widely studied. However, diffusion naturally drives the system towards an equilibrium state, leading to issues like over-smoothing. To this end, we propose GRADE inspired by graph aggregation-diffusion equations, which includes the delicate balance between nonlinear diffusion and aggregation induced by interaction potentials. The node representations obtained through agg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.20221","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/2403.20221/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":"2403.20221","created_at":"2026-07-05T08:02:15.900457+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.20221v1","created_at":"2026-07-05T08:02:15.900457+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.20221","created_at":"2026-07-05T08:02:15.900457+00:00"},{"alias_kind":"pith_short_12","alias_value":"QFMNEBYMSZTH","created_at":"2026-07-05T08:02:15.900457+00:00"},{"alias_kind":"pith_short_16","alias_value":"QFMNEBYMSZTHHVDM","created_at":"2026-07-05T08:02:15.900457+00:00"},{"alias_kind":"pith_short_8","alias_value":"QFMNEBYM","created_at":"2026-07-05T08:02:15.900457+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.00384","citing_title":"S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ","json":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ.json","graph_json":"https://pith.science/api/pith-number/QFMNEBYMSZTHHVDMHODJFPMTSQ/graph.json","events_json":"https://pith.science/api/pith-number/QFMNEBYMSZTHHVDMHODJFPMTSQ/events.json","paper":"https://pith.science/paper/QFMNEBYM"},"agent_actions":{"view_html":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ","download_json":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ.json","view_paper":"https://pith.science/paper/QFMNEBYM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.20221&json=true","fetch_graph":"https://pith.science/api/pith-number/QFMNEBYMSZTHHVDMHODJFPMTSQ/graph.json","fetch_events":"https://pith.science/api/pith-number/QFMNEBYMSZTHHVDMHODJFPMTSQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ/action/storage_attestation","attest_author":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ/action/author_attestation","sign_citation":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ/action/citation_signature","submit_replication":"https://pith.science/pith/QFMNEBYMSZTHHVDMHODJFPMTSQ/action/replication_record"}},"created_at":"2026-07-05T08:02:15.900457+00:00","updated_at":"2026-07-05T08:02:15.900457+00:00"}