It　is　important　to　estimate　the　global　network　traffic　data　from　partial　traffic　measurements　for　many　network　management　tasks,　including　status　monitoring　and　fault　detection.　However,　existing　estimation　approaches　cannot　well　handle　the　topological　correlations　hidden　in　network　traffic　and　suffer　from　limited　imputation　performance.　This　paper　proposes　a　deep　learning　approach　for　network　traffic　imputation,　which　well　exploits　the　topological　structure　of　network　traffic.　We　first　model　the　network　traffic　as　a　novel　graph-tensor　and　derive　a　theoretical　recovery　guarantee.　Then　we　develop　an　iterative　graph-tensor　completion　algorithm　and　propose　a　graph　neural　network　for　network　traffic　imputation　by　unfolding　the　iterative　algorithm.　The　proposed　graph　neural　network　well　captures　the　topological　correlations　of　network　traffic　and　achieves　accurate　imputation.　Extensive　experiments　on　real-world　datasets　show　that　the　proposed　graph　neural　network　achieves　about　one-half　lower　relative　square　error　while　at　least　ten　times　faster　imputation　speed　than　the　existing　methods.