As　an　essential　concept　in　attention,　context　defines　the　overall　scope　under　consideration.　In　attention-　based　GNNs,　context　becomes　the　set　of　representation　nodes　of　graph　embedding.　Current　approaches　choose　immediate　neighbors　of　the　target　or　its　subset　as　the　context,　which　limits　the　ability　of　attention　to　capture　long-distance　dependency.　To　address　this　deficiency,　we　propose　a　novel　attention-based　GNN　framework　with　extended　contexts.　Concretely,　multi-hop　nodes　are　first　selected　for　context　expansion　according　to　information　transferability　and　the　number　of　hops.　Then,　to　reduce　the　computational　cost　and　fit　the　graph　representation　learning　process,　two　heuristic　context　refinement　policies　are　designed　by　focusing　on　local　graph　structure.　One　is　for　the　graphs　with　high　degrees,　multi-hop　neighbors　with　fewer　connections　to　the　target　are　removed　to　acquire　accurate　diffused　information.　The　other　is　for　the　graphs　with　low　degrees　or　uniform　degree　distribution,　low-transferability　neighbors　are　dislodged　to　ensure　the　graph　locality　is　not　obscured　by　the　global　information　induced　by　the　extended　context.　Finally,　multi-head　attention　is　employed　in　the　refined　context.　Numerical　comparisons　with　23　baselines　demonstrate　the　superiority　of　our　method.　Extensive　model　analysis　shows　that　extending　context　with　the　informative　multi-hop　neighbors　properly　indeed　promotes　the　performance　of　attention-based　GNNs.