Recovery over Agreement: Cross-View Masked Graph Learning for Drug Repositioning

Computational drug repositioning seeks new indications for existing drugs, yet drug–disease association (DDA) prediction is hampered by sparse labels and by evidence scattered across heterogeneous entity attributes and auxiliary relations. Existing multi-view methods encode attribute- and relation-derived graphs independently and align them only through agreement objectives, which reward redundancy rather than complementarity; they typically leave supervised edges visible to relation message passing, and multi-task variants force DDA, drug–protein, and disease–protein prediction onto a single fused representation. We propose Cross-view Attribute–Relation Exchange (CARE), which replaces cross-view agreement with cross-view reconstruction. Under a single node- and edge-masking process applied only to the relation encoder, the relation view reconstructs masked entity representations. In contrast, the attribute view ranks masked associations above type-compatible unknown pairs, so each view must supply what the other withholds and target edges never enter relation message passing. A type-aware contrastive objective keeps entity semantics consistent across views, and task-conditioned gates let drug–protein and disease–protein supervision strengthen the shared encoders without imposing a common fusion on the target task. On three benchmarks, CARE consistently outperforms existing methods, with the largest gains in recall, indicating that our approach helps recover associations that sparse relational evidence alone misses and showing the benefit of coupling multi-task learning with reciprocal cross-view reconstruction for sparse DDA prediction.