Identifying　complex　human　diseases　at　molecular　level　is　very　helpful,　especially　in　diseases　diagnosis,　therapy,　prognosis　and　monitoring.　Accumulating　evidences　demonstrated　that　RNAs　are　playing　important　roles　in　identifying　various　complex　human　diseases.　However,　the　amount　of　verified　disease-related　RNAs　is　still　little　while　many　of　their　biological　experiments　are　very　time-consuming　and　labor-intensive.　Therefore,　researchers　have　instead　been　seeking　to　develop　effective　computational　algorithms　to　predict　associations　between　diseases　and　RNAs.　In　this　paper,　we　propose　a　novel　model　called　Graph　Attention　Adversarial　Network　(GAAN)　for　the　potential　disease-RNA　association　prediction.　To　our　best　knowledge,　we　are　among　the　pioneers　to　integrate　successfully　both　the　state-of-the-art　graph　convolutional　networks　(GCNs)　and　attention　mechanism　in　our　model　for　the　prediction　of　disease-RNA　associations.　Comparing　to　other　disease-RNA　association　prediction　methods,　GAAN　is　novel　in　conducing　the　computations　from　the　aspect　of　global　structure　of　disease-RNA　network　with　graph　embedding　while　integrating　features　of　local　neighborhoods　with　the　attention　mechanism.　Moreover,　GAAN　uses　adversarial　regularization　to　further　discover　feature　representation　distribution　of　the　latent　nodes　in　disease-RNA　networks.　GAAN　also　benefits　from　the　efficiency　of　deep　model　for　the　computation　of　big　associations　networks.　To　evaluate　the　performance　of　GAAN,　we　conduct　experiments　on　networks　of　diseases　associating　with　two　different　RNAs:　MicroRNAs　(miRNAs)　and　Long　non-coding　RNAs　(lncRNAs).　Comparisons　of　GAAN　with　several　popular　baseline　methods　on　disease-RNA　networks　show　that　our　novel　model　outperforms　others　by　a　wide　margin　in　predicting　potential　disease-RNAs　associations.