Due　to　the　extraordinary　abilities　in　extracting　complex　patterns,　graph　neural　networks　(GNNs)　have　demonstrated　strong　performances　and　received　increasing　attention　in　recent　years.　Despite　their　prominent　achievements,　recent　GNNs　do　not　pay　enough　attention　to　discriminate　nodes　when　determining　the　information　sources.　Some　of　them　select　information　sources　from　all　or　part　of　neighbors　without　distinction,　and　others　merely　distinguish　nodes　according　to　either　graph　structures　or　node　features.　To　solve　this　problem,　we　propose　the　concept　of　the　Influence　Set　and　design　a　novel　general　GNN　framework　called　the　graph　influence　network　(GINN),　which　discriminates　neighbors　by　evaluating　their　influences　on　targets.　In　GINN,　both　topological　structures　and　node　features　of　the　graph　are　utilized　to　find　the　most　influential　nodes.　More　specifically,　given　a　target　node,　we　first　construct　its　influence　set　from　the　corresponding　neighbors　based　on　the　local　graph　structure.　To　this　aim,　the　pairwise　influence　comparison　relations　are　extracted　from　the　paths　and　a　HodgeRank-based　algorithm　with　analytical　expression　is　devised　to　estimate　the　neighbors＇　structure　influences.　Then,　after　determining　the　influence　set,　the　feature　influences　of　nodes　in　the　set　are　measured　by　the　attention　mechanism,　and　some　task-irrelevant　ones　are　further　dislodged.　Finally,　only　neighbor　nodes　that　have　high　accessibility　in　structure　and　strong　task　relevance　in　features　are　chosen　as　the　information　sources.　Extensive　experiments　on　several　datasets　demonstrate　that　our　model　achieves　state-of-the-art　performances　over　several　baselines　and　prove　the　effectiveness　of　discriminating　neighbors　in　graph　representation　learning.