gSpan: Graph-Based Substructure Pattern Mining

We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining), which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depth-first search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes by an order of magnitude.

• Type: Conference
• Year: 2002
• Where: ICDM
• Authors: Xifeng Yan, Jiawei Han
• Keywords: Data Mining, Frequent Graph-based Pattern, Graph-based Substructure Pattern, ICDM 2002, Lexicographic Order