In　partial　multi-label　learning　(PML),　each　data　example　is　equipped　with　a　candidate　label　set,　which　consists　of　multiple　ground-truth　labels　and　other　false-positive　labels.　Recently,　graph-based　methods,　which　demonstrate　a　good　ability　to　estimate　accurate　confidence　scores　from　candidate　labels,　have　been　prevalent　to　deal　with　PML　problems.　However,　we　observe　that　existing　graph-based　PML　methods　typically　adopt　linear　multilabel　classifiers　and　thus　fail　to　achieve　superior　performance.　In　this　work,　we　attempt　to　remove　several　obstacles　for　extending　them　to　deep　models　and　propose　a　novel　deep　Partial　multi-Label　model　with　grAph-disambIguatioN　(PLAIN).　Specifically,　we　introduce　the　instance-level　and　label-level　similarities　to　recover　label　confidences　as　well　as　exploit　label　dependencies.　At　each　training　epoch,　labels　are　propagated　on　the　instance　and　label　graphs　to　produce　relatively　accurate　pseudo-labels;　then,　we　train　the　deep　model　to　fit　the　numerical　labels.　Moreover,　we　provide　a　careful　analysis　of　the　risk　functions　to　guarantee　the　robustness　of　the　proposed　model.　Extensive　experiments　on　various　synthetic　datasets　and　three　real-world　PML　datasets　demonstrate　that　PLAIN　achieves　significantly　superior　results　to　state-of-the-art　methods.　©　2023　International　Joint　Conferences　on　Artificial　Intelligence.　All　rights　reserved.