Graph sampling obtains graph structures of smaller size compared to the original graph by performing approximation operations on graph data,and thus serves downstream tasks such as graph analysis and graph visualisation.Existing graph sampling algorithms focus on preserving salient structural features in the graph and ignore node attributes,leading to difficulties in achieving the expected results of sampled graphs in many downstream tasks,such as frequent pattern mining.For this reason,this paper proposes Motif-Based Node Biased Sampling(MNBS),an algorithm that redefines the importance of nodes in the graph using frequent Motif substructures,followed by biased node sampling,achieving sampling that fuses node attributes with structural features.In order to quickly identify frequent Motif patterns,Fast Motif-Pattern Discovery(FMPD)algorithm with"early termination"is designed to efficiently and accurately discover Motif patterns to support graph sampling.Experiments show that the MNBS sampling algorithm outperforms the other baseline algorithms in a number of metrics.For example,the average reduction of Logarithm Normalized Cumulative Group Relevance is 0.54,and FMPD algorithm with the"early termination"feature reduces the time and memory consumption by 56.1％and 29.8％,respectively.
万方数据
维普
