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+and+Binary+Programming+(BP)+problems%252C+laying+the+groundwork+for+GNNs+to+approximate+solutions+for+these+computationally+challenging+problems.+By+analyzing+the+sensitivity+of+BP+problems%252C+we+are+able+to+frame+the+solution+of+BP+problems+as+a+heterophilic+node+classification+task.+We+then+propose+Binary-Programming+GNN+(BPGNN)%252C+an+architecture+that+integrates+graph+representation+learning+techniques+with+BP-aware+features+to+approximate+BP+solutions+efficiently.+Additionally%252C+we+introduce+a+self-supervised+data+generation+mechanism%252C+to+enable+efficient+and+tractable+training+data+acquisition+even+for+large-scale+BP+problems.+Experimental+evaluations+of+BPGNN+across+diverse+BP+problem+sizes+showcase+its+superior+performance+compared+to+exhaustive+search+and+heuristic+approaches.+Finally%252C+we+discuss+open+challenges+in+the+under-explored+field+of+BP+problems+with+GNNs.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.e6e13dd3c74a222f4f11e0e28d49a28e%3frik%3d%252bUC2jSNZ9JeB7Q%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Graph+Neural+Network+Bioinformatics&simid=607998779738817180&FORM=IRPRST&ck=B4BAA0234266947AF7BC217BAB5AB74E&selectedIndex=24&itb=0)
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