Xifeng Yan

TL;DR: A novel algorithm called gSpan (graph-based substructure pattern mining), which discovers frequent substructures without candidate generation by building a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label.

TL;DR: Under the meta path framework, a novel similarity measure called PathSim is defined that is able to find peer objects in the network (e.g., find authors in the similar field and with similar reputation), which turns out to be more meaningful in many scenarios compared with random-walk based similarity measures.

TL;DR: It is believed that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run, however, there are still some challenging research issues that need to be solved before frequent patternmining can claim a cornerstone approach in data mining applications.

TL;DR: A closed graph pattern mining algorithm, CloseGraph, is developed by exploring several interesting pruning methods and shows that it not only dramatically reduces unnecessary subgraphs to be generated but also substantially increases the efficiency of mining, especially in the presence of large graph patterns.

TL;DR: The gIndex approach not only provides and elegant solution to the graph indexing problem, but also demonstrates how database indexing and query processing can benefit form data mining, especially frequent pattern mining.