Sentence examples for frequent edges from inspiring English sources

Following from Definition 4, we know that it consists of frequent edges.

Such frequent edges form a stable network skeleton shared by majority of the solutions.

On the other hand, frequent edges in   D do not always jointly form frequent subgraph.

Edges that appear in a very small number of graphs will have low co-occurrence similarity with frequent edges and retaining these not-so-frequent edges will lead to a large summary graph and a very sparse edge occurrence matrix.

Moreover, edges that appear in a small number of graphs will not be in the edge cluster as their similarities with frequent edges can be below the β threshold.

For example, if we keep the frequent edges that appear in at least 7 graphs, we get a summary graph with with 9,784 nodes and 308,162 edges.

We proposed the MFMS algorithm [ 8] that first mines maximal frequent edge sets.

Figure 6 shows the effect of the edge frequency threshold on the percentage of frequent edge clusters.

Figure 3 shows the percentage of the frequent edge clusters that appear in at least N graphs.

It is clear that as we increase the edge frequency threshold, the percentage of frequent edge clusters (frequent in at least 7 graphs) increases.

In addition, the runtime for these approaches grows exponentially; even the most efficient ones, such as MULE [ 5] that enumerates maximal frequent edge sets, took almost 57 days for a set of 98 network instances (details available upon request).