# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 4 Experiments
### Experimental Results Analysis  
#### 4.3.1 FSM-BC-BSP Algorithm Results  
This section provides an in-depth analysis of the FSM-BC-BSP algorithm's performance under various input datasets and parameter configurations. The examined parameters primarily include minimum support, the number of steps in the super step, and the count of initial divisions. To gauge the impact of each parameter on the algorithm's performance, specific adjustments are made to one parameter at a time, keeping others constant. To ensure fairness, the output of the algorithm is consistently cleared from the HDFS with each run, \begin{table}
\begin{tabular}{c c c} \hline \hline \(D_{0}\) **(Figure 3b)** & \(D_{1}\) **(Figure 3c)** & \(D_{2}\) **(Figure 3d)** \\ \hline \((0,1,\) X, a, X) & \((0,1,\) X, a, X) & \((0,1,\) Y, b, X) \\ \((1,2,\) X, a, Z) & \((1,2,\) X, b, Y) & \((1,2,\) X, a, X) \\ \((2,0,\) Z, b, X) & \((1,3,\) X, a, Z) & \((2,3,\) X, b, Z) \\ \((1,3,\) X, b, Y) & \((3,0,\) Z, b, X) & \((3,\) 1, Z, a, X) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: DFS codes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Vertex** & **Edges** & **Vertices\_Label Number** & **Edge\_Label Number** \\ \hline As.txt & 6474 & 12,572 & 62 & 61 \\ DBLP & 151,574 & 191,840 & 7 & 17 \\ Credit1 & 14,700 & 14,000 & 59 & 20 \\ Credit2 & 6300 & 98,576 & 59 & 51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The description of the datasets. while maintaining consistent hardware conditions to eliminate additional factors affecting the performance. Different Datasets and Varied Minimum Support Corresponding to Subgraph Counts Employing the FSM-BC-BSP algorithm on the credit1, credit2, and DBLP datasets, frequent subgraphs are mined under varying minimum support levels. The subgraph counts are then tabulated based on different support degrees. With an initial division count of nine and a super step count of four, the results indicate that, as expected, the number of subgraphs decreases exponentially with an increasing support degree. This trend aligns with common expectations. Notably, for the DBLP dataset, no corresponding frequent subgraph is identified when the support exceeds 16, a characteristic specific to the DBLP dataset. The statistical results are shown in Table 3.  
Different Datasets, Varied Minimum Support, and Algorithm Runtime Applying the FSM-BC-BSP algorithm to the credit1, credit2, As, and DBLP datasets with varying support degrees, the runtime of the algorithm is statistically measured. With an initial fragment count of nine and a super step count of four, the algorithm demonstrates the efficient mining of frequent subgraphs across different support levels. Notably, the runtime is prolonged when the support is very small under the DBLP dataset. This extended runtime can be attributed to the uneven distribution of neighboring edges in DBLP vertices, following a power-law distribution. Such disparities lead to a large number of some frequent subgraphs and the minimal occurrence of others, resulting in inefficient resource utilization and an increased operating time, as depicted in Figure 5. The figure shows the trend of the algorithm based on the three datasets of credit1, credit2, and As.txt as the support running time changes. The running results based on DBLP are too different from the other three and cannot be displayed in the same graph. Different Datasets, Varied Initial Fragments, and Algorithm Runtime Examining the credit1, credit2, and AS datasets under the FSM-BC-BSP algorithm with a consistent support level of four and a super step count of five, the algorithm's runtime is evaluated under different initial fragment counts. The results indicate a trend of initial downward and subsequent upward runtime variations with changes in the number of partitions. This behavior is attributed to the reduced parallelism when fewer partitions are  
Figure 5: The times of frequent subgraphs with different support under different datasets.