{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7FYCU6RX3FP35EIOUF2QCFBIFT","short_pith_number":"pith:7FYCU6RX","schema_version":"1.0","canonical_sha256":"f9702a7a37d95fbe910ea1750114282cec4960d50a2594f7c1e1d0cdeb54e375","source":{"kind":"arxiv","id":"2507.10626","version":1},"attestation_state":"computed","paper":{"title":"Player-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Joachim Gudmundsson, Lintao Wang, Michael Horton, Shiwen Xu, Zhiyong Wang","submitted_at":"2025-07-14T06:43:36Z","abstract_excerpt":"Predicting soccer match outcomes is a challenging task due to the inherently unpredictable nature of the game and the numerous dynamic factors influencing results. While it conventionally relies on meticulous feature engineering, deep learning techniques have recently shown a great promise in learning effective player and team representations directly for soccer outcome prediction. However, existing methods often overlook the heterogeneous nature of interactions among players and teams, which is crucial for accurately modeling match dynamics. To address this gap, we propose HIGFormer (Heteroge"},"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":"2507.10626","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T06:43:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ad33d624c82656d3ec82bad33cf4bfdc4af2dc666a55040ed24e8938a3528cca","abstract_canon_sha256":"22030f0b55cda366bb504ca1177336bde45ab16471ec08a5377102e25f4d7758"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:57.791632Z","signature_b64":"wQTlBvhiMnSLIevcvaASxR4m2vRqIuemyy5eEBEKm0eVC0oSVD/944ip1pQ6KXb8w8vvCkZnwLSpEBy6hGFvCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9702a7a37d95fbe910ea1750114282cec4960d50a2594f7c1e1d0cdeb54e375","last_reissued_at":"2026-07-05T11:36:57.791134Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:57.791134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Player-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Joachim Gudmundsson, Lintao Wang, Michael Horton, Shiwen Xu, Zhiyong Wang","submitted_at":"2025-07-14T06:43:36Z","abstract_excerpt":"Predicting soccer match outcomes is a challenging task due to the inherently unpredictable nature of the game and the numerous dynamic factors influencing results. While it conventionally relies on meticulous feature engineering, deep learning techniques have recently shown a great promise in learning effective player and team representations directly for soccer outcome prediction. However, existing methods often overlook the heterogeneous nature of interactions among players and teams, which is crucial for accurately modeling match dynamics. To address this gap, we propose HIGFormer (Heteroge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.10626","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/2507.10626/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":"2507.10626","created_at":"2026-07-05T11:36:57.791193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.10626v1","created_at":"2026-07-05T11:36:57.791193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.10626","created_at":"2026-07-05T11:36:57.791193+00:00"},{"alias_kind":"pith_short_12","alias_value":"7FYCU6RX3FP3","created_at":"2026-07-05T11:36:57.791193+00:00"},{"alias_kind":"pith_short_16","alias_value":"7FYCU6RX3FP35EIO","created_at":"2026-07-05T11:36:57.791193+00:00"},{"alias_kind":"pith_short_8","alias_value":"7FYCU6RX","created_at":"2026-07-05T11:36:57.791193+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/7FYCU6RX3FP35EIOUF2QCFBIFT","json":"https://pith.science/pith/7FYCU6RX3FP35EIOUF2QCFBIFT.json","graph_json":"https://pith.science/api/pith-number/7FYCU6RX3FP35EIOUF2QCFBIFT/graph.json","events_json":"https://pith.science/api/pith-number/7FYCU6RX3FP35EIOUF2QCFBIFT/events.json","paper":"https://pith.science/paper/7FYCU6RX"},"agent_actions":{"view_html":"https://pith.science/pith/7FYCU6RX3FP35EIOUF2QCFBIFT","download_json":"https://pith.science/pith/7FYCU6RX3FP35EIOUF2QCFBIFT.json","view_paper":"https://pith.science/paper/7FYCU6RX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.10626&json=true","fetch_graph":"https://pith.science/api/pith-number/7FYCU6RX3FP35EIOUF2QCFBIFT/graph.json","fetch_events":"https://pith.science/api/pith-number/7FYCU6RX3FP35EIOUF2QCFBIFT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7FYCU6RX3FP35EIOUF2QCFBIFT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7FYCU6RX3FP35EIOUF2QCFBIFT/action/storage_attestation","attest_author":"https://pith.science/pith/7FYCU6RX3FP35EIOUF2QCFBIFT/action/author_attestation","sign_citation":"https://pith.science/pith/7FYCU6RX3FP35EIOUF2QCFBIFT/action/citation_signature","submit_replication":"https://pith.science/pith/7FYCU6RX3FP35EIOUF2QCFBIFT/action/replication_record"}},"created_at":"2026-07-05T11:36:57.791193+00:00","updated_at":"2026-07-05T11:36:57.791193+00:00"}