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+have+achieved+remarkable+success+in+a+wide+range+of+tasks+by+encoding+features+combined+with+topology+to+create+effective+representations.+However%252C+the+fundamental+problem+of+understanding+and+analyzing+how+graph+topology+influences+the+performance+of+learning+models+on+downstream+tasks+has+not+yet+been+well+understood.+In+this+paper%252C+we+propose+a+metric%252C+TopoInf%252C+which+characterizes+the+influence+of+graph+topology+by+measuring+the+level+of+compatibility+between+the+topological+information+of+graph+data+and+downstream+task+objectives.+We+provide+analysis+based+on+the+decoupled+GNNs+on+the+contextual+stochastic+block+model+to+demonstrate+the+effectiveness+of+the+metric.+Through+extensive+experiments%252C+we+demonstrate+that+TopoInf+is+an+effective+metric+for+measuring+topological+influence+on+corresponding+tasks+and+can+be+further+leveraged+to+enhance+graph+learning.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.d4aeed42a67937f655a948ea4654b47a%3frik%3dgBpoAjhnovQD1g%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Topology+of+Machine+Learning+Field&FORM=IRPRST&ck=2EAEA3A5126C78ABBEE39DB1120AF7AA&selectedIndex=19&itb=0)
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