  While there have been lots of work studying frequent subgraph mining, very
rare publications have discussed frequent subnet mining from more complicated
data structures such as Petri nets. This paper studies frequent subnets mining
from a single large Petri net. We follow the idea of transforming a Petri net
in net graph form and to mine frequent sub-net graphs to avoid high complexity.
Technically, we take a minimal traversal approach to produce a canonical label
of the big net graph. We adapted the maximal independent embedding set approach
to the net graph representation and proposed an incremental pattern growth
(independent embedding set reduction) way for discovering frequent sub-net
graphs from the single large net graph, which are finally transformed back to
frequent subnets. Extensive performance studies made on a single large Petri
net, which contains 10K events, 40K conditions and 30 K arcs, showed that our
approach is correct and the complexity is reasonable.
