AMore Experimental Results

A.1 Comparison with SOTAModels on 60%/20%/20% Random Splits The main results of the full sets of experiments 17 with statistics of datasets are summarized in Table 2, where we report the mean accuracy (%) and standard deviation. We can see that after applied in ACM or ACMII framework, the performance of baseline models are boosted on almost all tasks and achieve SOTA performance on 9out of 10datasets. Especially, ACMII-GCN+ performs the best in terms of average rank (4.40) across all datasets. Overall, It suggests that ACM or ACMII framework can significantly increase the performance of GNNs on node classification tasks on heterophilic graphs and maintain highly competitive performance on homophilic datasets. The best results are highlighted in grey and the best baseline results (SOTA in Figure 6) are underlined. Results "*" are reported from [8, 26] and results " " are from [36].