Recent　studies　have　shown　that　Graph　Transformers　(GTs)　can　be　effective　for　specific　graph-level　tasks.　However,　when　it　comes　to　node　classification,　training　GTs　remains　challenging,　especially　in　semi-supervised　settings　with　a　severe　scarcity　of　labeled　data.　Our　paper　aims　to　address　this　research　gap　by　focusing　on　semi-supervised　node　classification.　To　accomplish　this,　we　develop　a　curriculum-enhanced　attention　distillation　method　that　involves　utilizing　a　Local　GT　teacher　and　a　Global　GT　student.　Additionally,　we　introduce　the　concepts　of　in-class　and　out-of-class　and　then　propose　two　improvements,　out-of-class　entropy　and　top-k　pruning,　to　facilitate　the　student＇s　out-of-class　exploration　under　the　teacher＇s　in-class　guidance.　Taking　inspiration　from　human　learning,　our　method　involves　a　curriculum　mechanism　for　distillation　that　initially　provides　strict　guidance　to　the　student　and　gradually　allows　for　more　out-of-class　exploration　by　a　dynamic　balance.　Extensive　experiments　show　that　our　method　outperforms　many　state-of-the-art　methods　on　seven　public　graph　benchmarks,　proving　its　effectiveness.　©　2024　12th　International　Conference　on　Learning　Representations,　ICLR　2024.　All　rights　reserved.