{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:O3PSKRFJ7Y32SOQZMPJU2F5WEF","short_pith_number":"pith:O3PSKRFJ","canonical_record":{"source":{"id":"2606.19825","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T06:05:08Z","cross_cats_sorted":[],"title_canon_sha256":"4e9dd34dc33a4572c1273c4812c505d8883baa54d6c53bcc8da312e9c009c95c","abstract_canon_sha256":"ca493df27e557f6b7a26e8e365094a19b1ca1a8f6922b12319524b8f67827cda"},"schema_version":"1.0"},"canonical_sha256":"76df2544a9fe37a93a1963d34d17b6217ebf91166ceb497871a942084699d0f6","source":{"kind":"arxiv","id":"2606.19825","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.19825","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"arxiv_version","alias_value":"2606.19825v1","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.19825","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"pith_short_12","alias_value":"O3PSKRFJ7Y32","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"pith_short_16","alias_value":"O3PSKRFJ7Y32SOQZ","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"pith_short_8","alias_value":"O3PSKRFJ","created_at":"2026-06-19T16:12:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:O3PSKRFJ7Y32SOQZMPJU2F5WEF","target":"record","payload":{"canonical_record":{"source":{"id":"2606.19825","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T06:05:08Z","cross_cats_sorted":[],"title_canon_sha256":"4e9dd34dc33a4572c1273c4812c505d8883baa54d6c53bcc8da312e9c009c95c","abstract_canon_sha256":"ca493df27e557f6b7a26e8e365094a19b1ca1a8f6922b12319524b8f67827cda"},"schema_version":"1.0"},"canonical_sha256":"76df2544a9fe37a93a1963d34d17b6217ebf91166ceb497871a942084699d0f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:36.305523Z","signature_b64":"XmAPDAOCFgmHJWhM11F1TISzkon1mgtT0rdMlkfxPTXkrRfPz+CmFYlszORnh2gZZfLgujrvzQFy9JoVtSPpDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76df2544a9fe37a93a1963d34d17b6217ebf91166ceb497871a942084699d0f6","last_reissued_at":"2026-06-19T16:12:36.305161Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:36.305161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.19825","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-19T16:12:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vHxuVTuF+xoLp99hsl/iLTWRCdccLEG2diixYpX/gRhJrEszQgzHZXvpQUZAcHr7dmcd/UPA+aISTrGjb9bJAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T10:01:23.001453Z"},"content_sha256":"611d6b8cf4cbce3a25082819c596f39da75b56c8e17de3d600de401eecf22ec8","schema_version":"1.0","event_id":"sha256:611d6b8cf4cbce3a25082819c596f39da75b56c8e17de3d600de401eecf22ec8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:O3PSKRFJ7Y32SOQZMPJU2F5WEF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Graph Neural Networks Using Proximity Graphs for Dust Source Emission Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ali Darvishi Boloorani, Ali Vefghi, Maryam Sanisales, Zahed Rahmati","submitted_at":"2026-06-18T06:05:08Z","abstract_excerpt":"Accurate prediction of dust source emissions is critical for mitigating the significant environmental and health hazards posed by dust storms. Traditional forecasting methods often struggle to capture the complex spatiotemporal dynamics of these phenomena.\n  In this paper, we demonstrate that proximity graphs enable Graph Neural Networks (GNNs) to effectively model the intricate spatial and temporal relationships between data points. Specifically, we use proximity graphs--such as Delaunay triangulation, Gabriel graph, k-Nearest Neighbor graph, and Yao graph--as the input for GNNs (including Gr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19825","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/2606.19825/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-19T16:12:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3LLF5RAfZST6oFwdSLkrsAkgv+DyKpoMckxH77Z7Y2VTlRQT8q81h/7LvIrf5kQnNiwHsDozF9yf43HFKav/BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T10:01:23.001818Z"},"content_sha256":"57dbc5fe7b586d38859a533e2a7308698c941496120b933fb31de9a9ff04f77a","schema_version":"1.0","event_id":"sha256:57dbc5fe7b586d38859a533e2a7308698c941496120b933fb31de9a9ff04f77a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O3PSKRFJ7Y32SOQZMPJU2F5WEF/bundle.json","state_url":"https://pith.science/pith/O3PSKRFJ7Y32SOQZMPJU2F5WEF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O3PSKRFJ7Y32SOQZMPJU2F5WEF/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-03T10:01:23Z","links":{"resolver":"https://pith.science/pith/O3PSKRFJ7Y32SOQZMPJU2F5WEF","bundle":"https://pith.science/pith/O3PSKRFJ7Y32SOQZMPJU2F5WEF/bundle.json","state":"https://pith.science/pith/O3PSKRFJ7Y32SOQZMPJU2F5WEF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O3PSKRFJ7Y32SOQZMPJU2F5WEF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:O3PSKRFJ7Y32SOQZMPJU2F5WEF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ca493df27e557f6b7a26e8e365094a19b1ca1a8f6922b12319524b8f67827cda","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T06:05:08Z","title_canon_sha256":"4e9dd34dc33a4572c1273c4812c505d8883baa54d6c53bcc8da312e9c009c95c"},"schema_version":"1.0","source":{"id":"2606.19825","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.19825","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"arxiv_version","alias_value":"2606.19825v1","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.19825","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"pith_short_12","alias_value":"O3PSKRFJ7Y32","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"pith_short_16","alias_value":"O3PSKRFJ7Y32SOQZ","created_at":"2026-06-19T16:12:36Z"},{"alias_kind":"pith_short_8","alias_value":"O3PSKRFJ","created_at":"2026-06-19T16:12:36Z"}],"graph_snapshots":[{"event_id":"sha256:57dbc5fe7b586d38859a533e2a7308698c941496120b933fb31de9a9ff04f77a","target":"graph","created_at":"2026-06-19T16:12:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.19825/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate prediction of dust source emissions is critical for mitigating the significant environmental and health hazards posed by dust storms. Traditional forecasting methods often struggle to capture the complex spatiotemporal dynamics of these phenomena.\n  In this paper, we demonstrate that proximity graphs enable Graph Neural Networks (GNNs) to effectively model the intricate spatial and temporal relationships between data points. Specifically, we use proximity graphs--such as Delaunay triangulation, Gabriel graph, k-Nearest Neighbor graph, and Yao graph--as the input for GNNs (including Gr","authors_text":"Ali Darvishi Boloorani, Ali Vefghi, Maryam Sanisales, Zahed Rahmati","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T06:05:08Z","title":"Enhancing Graph Neural Networks Using Proximity Graphs for Dust Source Emission Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19825","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:611d6b8cf4cbce3a25082819c596f39da75b56c8e17de3d600de401eecf22ec8","target":"record","created_at":"2026-06-19T16:12:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ca493df27e557f6b7a26e8e365094a19b1ca1a8f6922b12319524b8f67827cda","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T06:05:08Z","title_canon_sha256":"4e9dd34dc33a4572c1273c4812c505d8883baa54d6c53bcc8da312e9c009c95c"},"schema_version":"1.0","source":{"id":"2606.19825","kind":"arxiv","version":1}},"canonical_sha256":"76df2544a9fe37a93a1963d34d17b6217ebf91166ceb497871a942084699d0f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"76df2544a9fe37a93a1963d34d17b6217ebf91166ceb497871a942084699d0f6","first_computed_at":"2026-06-19T16:12:36.305161Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:12:36.305161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XmAPDAOCFgmHJWhM11F1TISzkon1mgtT0rdMlkfxPTXkrRfPz+CmFYlszORnh2gZZfLgujrvzQFy9JoVtSPpDg==","signature_status":"signed_v1","signed_at":"2026-06-19T16:12:36.305523Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.19825","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:611d6b8cf4cbce3a25082819c596f39da75b56c8e17de3d600de401eecf22ec8","sha256:57dbc5fe7b586d38859a533e2a7308698c941496120b933fb31de9a9ff04f77a"],"state_sha256":"e8f67ec30bb96b2956fa80ceae3a10ac4a2a654af3eab7e3e2d57bd35a9468a6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J9uECisfycNTJJIJlfFAp76GW8/vCrdtgxYpBEr778nZow0xda7xgzi9cy1SY4fYsnXKvXefOIi/Ykr23kneCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T10:01:23.003682Z","bundle_sha256":"815d2c3ea4372879e7284fcef551fa3dea465239b9a2c31d1b500792264b32f7"}}