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%252C+and+the+cluster+indices+computed+by+our+recursive+spectral+bisection+(RSB)+algorithm.+These+measures+are+flattened+over+the+one+dimensional+vector+space+into+their+respective+sub-component+ranges+such+that+the+entire+set+of+vector+similarity+functions+could+be+used+for+finding+similar+nodes.+The+error+is+defined+by+the+sum+of+pairwise+square+differences+across+a+randomly+selected+sample+of+graph+nodes+between+the+assumed+embeddings+and+the+ground+truth+estimates+as+our+novel+loss+function.+The+ground+truth+is+estimated+to+be+a+combination+of+pairwise+Jaccard+similarity+and+the+number+of+overlapping+labels.+Finally%252C+we+demonstrate+a+multi-variate+stochastic+gradient+descent+(SGD)+algorithm+to+compute+the+weighing+factors+among+sub-vector+spaces+to+minimize+the+average+error+using+a+random+sampling+logic.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.7ccdab1e6a4bca4d84f5ed835df4d4c5%3frik%3dQfnHac2WlYTjXw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Graph+Embedding+Horst+Bunke&FORM=IRPRST&ck=A662531D71140200EE1DC880C4DA556C&selectedIndex=16&itb=0)
