Domain　adaptation　aims　to　leverage　information　from　the　source　domain　to　improve　the　classification　performance　in　the　target　domain.　It　mainly　utilizes　two　schemes:　sample　reweighting　and　feature　matching.　While　the　first　scheme　allocates　different　weights　to　individual　samples,　the　second　scheme　matches　the　feature　of　two　domains　using　global　structural　statistics.　The　two　schemes　are　complementary　with　each　other,　which　are　expected　to　jointly　work　for　robust　domain　adaptation.　Several　methods　combine　the　two　schemes,　but　the　underlying　relationship　of　samples　is　insufficiently　analyzed　due　to　the　neglect　of　the　hierarchy　of　samples　and　the　geometric　properties　between　samples.　To　better　combine　the　advantages　of　the　two　schemes,　we　propose　a　Grassmannian　graph-attentional　landmark　selection　(GGLS)　framework　for　domain　adaptation.　GGLS　presents　a　landmark　selection　scheme　using　attention-induced　neighbors　of　the　graphical　structure　of　samples　and　performs　distribution　adaptation　and　knowledge　adaptation　over　Grassmann　manifold.　the　former　treats　the　landmarks　of　each　sample　differently,　and　the　latter　avoids　feature　distortion　and　achieves　better　geometric　properties.　Experimental　results　on　different　real-world　cross-domain　visual　recognition　tasks　demonstrate　that　GGLS　provides　better　classification　accuracies　compared　with　state-of-the-art　domain　adaptation　methods.