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DRIVE is a semi\u2010supervised graph framework integrating mutation frequency, copy\u2010number aberration, DNA methylation, and gene expression with biological networks. It separates PPI neighborhoods into tight and loose semantic views based on learnable representation consistency, reducing cross\u2010class signal mixing. Multi\u2010omics evidence is decomposed into omics\u2010common and omics\u2010specific components through contrastive mutual\u2010information learning and the soft orthogonality constraint. Joint training combines self\u2010supervised learning with focal and max\u2010margin objectives to improve prioritization under sparse, imbalanced annotations. Across six benchmark PPI networks, DRIVE outperforms ten methods, achieving mean areas under the precision\u2013recall curve (AUPRC) and receiver operating characteristic curve (AUROC) of 0.9204 and 0.9704, respectively. 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Pharmacogenomic associations further suggest therapeutic vulnerabilities.<\/jats:p>","DOI":"10.1002\/advs.77970","type":"journal-article","created":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T18:00:31Z","timestamp":1790532031000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Disentangling Heterogeneous Molecular Networks for Multi\u2010Omics\u2010Driven Cancer Driver Discovery"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9490-638X","authenticated-orcid":false,"given":"Xinjing","family":"Gong","sequence":"first","affiliation":[{"name":"School of Life Science and Technology, Faculty of Life Sciences and Medicine Harbin Institute of Technology  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4187-7610","authenticated-orcid":false,"given":"Ji","family":"Li","sequence":"additional","affiliation":[{"name":"School of Life 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