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Given a trained deep graph convolution network (GCN), how can we effectively compress it into a compact network without significant loss of accuracy? Compressing a trained deep GCN into a compact GCN is of great importance for implementing the model to environments such as mobile or embedded systems, which have limited computing resources. However, previous works for compressing deep GCNs do not consider the multi-hop aggregation of the deep GCNs, though it is the main purpose for theirdoi:10.1371/journal.pone.0256187 pmid:34388224 pmcid:PMC8363007 fatcat:cv75gwtcnfbc7luszqgvqpcpqm