## 5 Experiments
### Frequent Approximate Subgraphs Mined by the Proposed Algorithm  
To demonstrate how the proposed algorithm mines frequent approximate subgraphs with structural and label variations, we applied ACGM-SLV to the small graph collection shown in Figure 4, varying the dissimilarity threshold (\(\beta\)) and introducing different label similarities in the node dictionary (\(\gamma\)) and edge dictionary (\(\delta\)). In a first experiment, we set the support threshold (\(\alpha\)) to 100% and the dissimilarity threshold (\(\beta\)) to zero, resulting in ACGM-SLV mining one frequent subgraph,, as shown in Table 2. Since no modifications are allowed, this frequent subgraph has an exact match. In a second experiment, we set the support threshold (\(\alpha\)) to 100% and the dissimilarity threshold (\(\beta\)) to one, resulting in ACGM-SLV mining the additional frequent approximate subgraph,, having two frequent subgraphs in total, as shown in the first column of Table 3. These frequent approximate subgraphs do not present structural variations, as only a single modification in the occurrences is allowed. This modification can be a label substitution on a node or edge. To demonstrate the label substitution, consider the frequent approximate subgraph, which appears on \(g_{2}\), the occurrence, where we have a node label substitution: the node label 'A' is substituted by the node label 'Z'. Additionally, the frequent approximate subgraph  has two occurrences with edge label substitutions on \(g_{3}\):, and, as. In a third experiment, we set the support threshold (\(\alpha\)) to 100% and increased the dissimilarity threshold (\(\beta\)) to two to allow for structural variations. With these parameter values, ACGM-SLV mines five additional frequent approximate subgraphs, having a total of seven mined frequent subgraphs, as shown in the first column of Table 4.  
To demonstrate the structural variations, consider the frequent approximate subgraph, and its occurrence  on \(g_{2}\), which requires two structural variations are required: the deletion of node, and its corresponding edge. In a fourth experiment, we set the support threshold (\(\alpha\)) to 100% and the dissimilarity threshold (\(\beta\)) to two while providing label similarities in nodes through the node dictionary(\(\gamma\)). This indicates that the node label 'Z' has 50% similarity with node label 'A' and node label 'Y' has 50% similarity with node label 'A'. In this scenario, ACGM-SLV mines the seven frequent approximate subgraphs shown in the first column of Table 4, plus one frequent approximate subgraph shown in the first column of Table 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Frequent Subgraph** & **Occurrences** \\ \hline \hline
**\⃝** & \⃝** & \⃝** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Frequent subgraphs mined by ACGM-SLV in the graph collection of Figure 4 with \(\alpha=100\%\) and \(\beta=0\). Figure 4: Graph collection consisting of three graphs used to demonstrate how the proposed algorithm mined frequent approximate subgraphs with structural and label variations. In this experiment, the small graph collection depicted in Figure 5 was used to illustrate the differences between the frequent approximate subgraphs mined by both algorithms. The parameters set in both algorithms were a support threshold (\(\alpha\)) of 100% and a dissimilarity threshold (\(\beta\)) of two, resulting in AGCM-SLV mining 31 frequent subgraphs, while REAFUM mined 20 frequent subgraphs. The results also indicated both algorithms mined 19 frequent approximate subgraphs in common; AGCM-SLV mined 12 additional frequent approximate subgraphs that REAFUM did not mine, while REAFUM mined one additional frequent approximate subgraph that AGCM-SLV did not mine.