Node　classification　is　an　important　task　in　graph　neural　networks,　but　most　existing　studies　assume　that　samples　from　different　classes　are　balanced.　However,　the　class　imbalance　problem　is　widespread　and　can　seriously　affect　the　model＇s　performance.　Reducing　the　adverse　effects　of　imbalanced　datasets　on　model　training　is　crucial　to　improve　the　model＇s　performance.　Therefore,　a　new　loss　function　FD-Loss　is　reconstructed　based　on　the　traditional　algorithm-level　approach　to　the　imbalance　problem.　Firstly,　we　propose　sample　mismeasurement　distance　to　filter　edge-hard　samples　and　simple　samples　based　on　the　distribution.　Then,　the　weight　coefficients　are　defined　based　on　the　mismeasurement　distance　and　used　in　the　loss　function　weighting　term,　so　that　the　loss　function　focuses　only　on　valuable　samples.　Experiments　on　several　benchmarks　demonstrate　that　our　loss　function　can　effectively　solve　the　sample　node　imbalance　problem　and　improve　the　classification　accuracy　by　4%　compared　to　existing　methods　in　the　node　classification　task.　©　2022　Association　for　Computing　Machinery.