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+have+demonstrated+remarkable+insights+into+the+relational+dynamics+of+the+world%252C+leading+to+the+crucial+question%3a+how+do+these+models+acquire+an+understanding+of+world+hybrid+relations%253F+Traditional+statistical+learning%252C+particularly+for+prediction+problems%252C+may+overlook+the+rich+and+inherently+structured+information+from+the+data%252C+especially+regarding+the+relationships+between+objects.+We+introduce+a+mathematical+model+that+formalizes+relational+learning+as+hypergraph+recovery+to+study+pre-training+of+FMs.+In+our+framework%252C+the+world+is+represented+as+a+hypergraph%252C+with+data+abstracted+as+random+samples+from+hyperedges.+We+theoretically+examine+the+feasibility+of+a+Pre-Trained+Model+(PTM)+to+recover+this+hypergraph+and+analyze+the+data+efficiency+in+a+minimax+near-optimal+style.+By+integrating+rich+graph+theories+into+the+realm+of+PTMs%252C+our+mathematical+framework+offers+powerful+tools+for+an+in-depth+understanding+of+pre-training+from+a+unique+perspective+and+can+be+used+under+various+scenarios.+As+an+example%252C+we+extend+the+framework+to+entity+alignment+in+multimodal+learning.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.3cde5109a7a63690ea1f105752c596bd%3frik%3d8ona3SBWrGmRfQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Strategies+for+Pre+Trained+Graph+Models+Paper&FORM=IRPRST&ck=556F3C136AFE4A046EA7F6F402556A3B&selectedIndex=4&itb=0)
