854a9ab0f323b841955e70ca383b27d1-Supplemental-Conference.pdf

To be specific, for every node in thei-th514 class, we use a binomial distribution with meanpin = hin/hto generate ah-dimensional binary515 vector as its((i 1) h+1)-th to (i h)-th attributes, and generated the rest attributes using516 a binomial distribution with meanpout = hout/(3h). In our experiments, we set4h = 200 and517 hout = 4(hin +hout = 16), so thatpin > pout, theh-dimensional attributes are associated with518 thei-th class with ahigher probability,whereas the rest3hattributes are irrelevant. Finally,537 we show the influence of the number of data augmentation in Figure 6 (d). With the increase of538 S,the node classification performance improvessteadily until stabilizes. Local567 variation algorithms differ only in the type of contraction sets that they consider: Variation Edges568 only contracts edges, whereas contraction sets in Variation Neighborhoods are subsets of nodes'569 neighborhood. Then, ANS-GT combines theweighted576 16 Table 6: Efficiencycomparisons with Graph Transfomer baselines.