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%252C+i.e.+neural+network+architectures+designed+for+learning+graph+representations%252C+have+become+a+popular+paradigm.+With+these+models+being+usually+characterized+by+intuition-driven+design+or+highly+intricate+components%252C+placing+them+within+the+theoretical+analysis+framework+to+distill+the+core+concepts%252C+helps+understand+the+key+principles+that+drive+the+functionality+better+and+guide+further+development.+Given+this+surge+in+interest%252C+this+article+provides+a+comprehensive+summary+of+the+theoretical+foundations+and+breakthroughs+concerning+the+approximation+and+learning+behaviors+intrinsic+to+prevalent+graph+learning+models.+Encompassing+discussions+on+fundamental+aspects+such+as+expressiveness+power%252C+generalization%252C+optimization%252C+and+unique+phenomena+such+as+over-smoothing+and+over-squashing%252C+this+piece+delves+into+the+theoretical+foundations+and+frontier+driving+the+evolution+of+graph+learning.+In+addition%252C+this+article+also+presents+several+challenges+and+further+initiates+discussions+on+possible+solutions.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.2f2f0ddf83af01ee3bda27994408a475%3frik%3dzOAO%252bN0IguSokw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Statistical+Learning+Graph+Theory&FORM=IRPRST&ck=36EB08ED29B489DEA974ADA8C3D1FC85&selectedIndex=34&itb=0)
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