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The result is shown in Table 4 for the most frequent subgraph.
It is essential for the computational tractability of most frequent subgraph discovery algorithms [ 3].
In summary, our experiments in this section demonstrate that SiS can identify most frequent subgraph for a broad set of parameter values.
Having defined the concept of frequency of a subgraph, we now formally define the concept of the most frequent subgraph in D.
Which also proved that motif is not necessarily the most frequent subgraph in the original probabilistic networks, but the subgraph in a original probability with much greater frequency than that in a random network.
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This supports our earlier conjecture that most probable subgraphs and most frequent subgraphs often are the same or comparable in terms of their frequency.
For SCE with the forbidden subgraph formulation, we see that the strengthened inequalities such as Constraint (3) allow to solve more instances, and that using the P5 (in our instances the most frequent forbidden subgraph) not only as a forbidden subgraph but also as a cutting plane further improves running time.
A size-k subgraph G in D is the most frequent size-k subgraph if f (G, D ) ≥ f (G ', D ) for any size-k subgraph G' in D.
Most of the frequent subgraph mining approaches are unsupervised.
Definition 3 (Most Frequent Size- k Subgraph).
The top-n most frequent size-k subgraphs in D is the set of n subgraphs T n with the largest frequency in D.
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