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Transformer PFvs VRGraph
+hierarchy+offer+a+theoretically+well-understood+expressive+power.+However%252C+such+architectures+often+fail+to+deliver+solid+predictive+performance+on+real-world+tasks%252C+limiting+their+practical+impact.+In+contrast%252C+global+attention-based+models+such+as+graph+transformers+demonstrate+strong+performance+in+practice%252C+but+comparing+their+expressive+power+with+the+k-WL+hierarchy+remains+challenging%252C+particularly+since+these+architectures+rely+on+positional+or+structural+encodings+for+their+expressivity+and+predictive+performance.+To+address+this%252C+we+show+that+the+recently+proposed+Edge+Transformer%252C+a+global+attention+model+operating+on+node+pairs+instead+of+nodes%252C+has+at+least+3-WL+expressive+power.+Empirically%252C+we+demonstrate+that+the+Edge+Transformer+surpasses+other+theoretically+aligned+architectures+regarding+predictive+performance+while+not+relying+on+positional+or+structural+encodings.+Our+code+is+available+at+https%3a%252F%252Fgithub.com%252Fluis-mueller%252Ftowards-principled-gts&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.41f109aaa773409903eb1855d4513c90%3frik%3dbArzF64gSVdLdg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Transformer+PF+vs+VR+Graph&simid=608044199128681135&FORM=IRPRST&ck=AC4F4396B9BAB6E26E88B0CD07A40AA8&selectedIndex=23&itb=0)
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