Modeling　and　analysis　of　complex　social　networks　is　an　important　topic　in　social　computing.　Graph　convolutional　networks　(GCNs)　are　widely　used　for　learning　social　network　embeddings　and　social　network　analysis.　However,　real-world　complex　social　networks,　such　as　Facebook　and　Math,　exhibit　significant　global　structural　and　dynamic　characteristics　that　are　not　adequately　captured　by　conventional　GCN　models.　To　address　the　above　issues,　this　paper　proposes　a　novel　graph　convolutional　network　considering　global　structural　features　and　global　temporal　dependencies　(GSTGCN).　Specifically,　we　innovatively　design　a　graph　coarsening　strategy　based　on　the　importance　of　social　membership　to　construct　a　dynamic　diffusion　process　of　graphs.　This　dynamic　diffusion　process　can　be　viewed　as　using　higher-order　subgraph　embeddings　to　guide　the　generation　of　lower-order　subgraph　embeddings,　and　we　model　this　process　using　gate　recurrent　unit　(GRU)　to　extract　comprehensive　global　structural　features　of　the　graph　and　the　evolutionary　processes　embedded　among　subgraphs.　Furthermore,　we　design　a　new　evolutionary　strategy　that　incorporates　a　temporal　self-attention　mechanism　to　enhance　the　extraction　of　global　temporal　dependencies　of　dynamic　networks　by　GRU.　GSTGCN　outperforms　current　state-of-the-art　network　embedding　methods　in　important　social　networks　tasks　such　as　link　prediction　and　financial　fraud　identification.　©　2020　Tsinghua　University　Press.