Mining tree-query associations in graphs

New applications of data mining, such as in biology, bioinformatics, or sociology, are faced with large datasets structured as graphs. We introduce a novel class of tree-shaped patterns called tree queries, and present algorithms for mining tree queries and tree-query associations in a large data graph. Novel about our class of patterns is that they can contain constants, and can contain existential nodes which are not counted when determining the number of occurrences of the pattern in the data graph. Our algorithms have a number of provable optimality properties, which are based on the theory of conjunctive database queries. We propose a practical, database-oriented implementation in SQL, and show that the approach works in practice through experiments on data about food webs, protein interactions, and citation analysis.