Mining globally distributed frequent subgraphs in a single labeled graph
Recent years have observed increasing efforts on graph mining and many algorithms have been developed for this purpose. However, most of the existing algorithms are designed for discovering frequent subgraphs in a set of labeled graphs only. Also, the few algorithms that find frequent subgraphs in a single labeled graph typically identify subgraphs appearing regionally in the input graph. In contrast, for real-world applications, it is commonly required that the identified frequent subgraphs in a single labeled graph should also be globally distributed. This paper thus fills this crucial void by proposing a new measure, termed G-Measure, to find globally distributed frequent subgraphs, called G-Patterns, in a single labeled graph. Specifically, we first show that the G-Patterns, selected by G-Measure, tend to be globally distributed in the input graph. Then, we present that G-Measure has the downward closure property, which guarantees the G-Measure value of a G-Pattern is not less than those of its supersets. Consequently, a G-Miner algorithm is developed for finding G-Patterns. Experimental results on four synthetic and seven real-world data sets and comparison with the existing algorithms demonstrate the efficacy of the G-Measure and the G-Miner for finding G-Patterns. Finally, an application of the G-Patterns is given.
g-measure g-pattern frequent subgraph mining