The　social　trust　assessment　can　spur　extensive　applications　such　as　social　recommendations,　shopping,　financial　investment　strategies,　etc,　but　remain　a　challenging　problem　having　limited　exploration.　Such　explorations　mainly　limit　their　studies　to　static　network　topology　or　simplified　dynamic　networks,　toward　the　social　trust　relationship　prediction.　In　contrast,　in　this　paper,　we　explore　the　social　trust　by　taking　into　account　the　time-varying　online　social　networks　whereas　the　social　trust　relationship　may　vary　over　time.　The　DTrust,　a　dynamic　graph　neural　network-based　solution,　will　be　proposed　for　accurate　social　trust　prediction.　In　particular,　DTrust　is　composed　of　a　static　aggregation　unit　and　a　dynamic　unit,　respectively　responsible　for　capturing　both　the　spatial　dependence　features　and　temporal　dependence　features.　In　the　former　unit,　we　stack　multiple　NNConv　layers　derived　from　the　edge-conditioned　convolution　network　for　capturing　the　spatial　dependence　features　correlated　to　the　network　topology　and　the　observed　social　relationships.　In　the　latter　unit,　a　gated　recurrent　unit　(GRU)　is　employed　for　learning　the　evolution　law　of　social　interaction　and　social　trust　relationships.　Based　on　the　extracted　spatial　and　temporal　features,　we　then　employ　a　fully　connected　neural　network　for　learning,　able　to　predict　the　social　trust　relationships　for　both　current　and　future　time　slots.　Extensive　experimental　results　exhibit　that　our　DTrust　can　outperform　the　benchmark　counterparts　on　two　real-world　datasets.　©　2023　IEEE.