## 4 Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Cov. (\%)**} & \multicolumn{4}{c}{**Runtime (S)**} & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \cline{3-8}  & & **The Proposed Scheme** & **GraphZip** [11]** & **SUBDUE** [27] & **The Proposed Scheme** & **GraphZip** [11] & **SUBDUE** [27] \\ \hline \multirow{3}{*}{3-CLIQ} & 20 & 58 & 52 & 68.3 & 100 & 100 & 89 \\ \cline{2-8}  & 50 & 4.2 & 3.8 & 23.7 & 100 & 100 & 90 \\ \cline{2-8}  & 80 & 4.1 & 3.7 & 12.1 & 100 & 100 & 87 \\ \hline \multirow{3}{*}{4-PATH} & 20 & 49 & 45.4 & 58.9 & 100 & 100 & 100 \\ \cline{2-8}  & 50 & 4.1 & 3 & 19.4 & 100 & 100 & 100 \\ \cline{2-8}  & 80 & 3.3 & 2.9 & 11.7 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{4-STAR} & 20 & 54 & 50.1 & Time Over & 100 & 100 & - \\ \cline{2-8}  & 50 & 4.5 & 4.1 & Time Over & 100 & 99.8 & - \\ \cline{2-8}  & 80 & 4.9 & 4.4 & Time Over & 100 & 100 & - \\ \hline \multirow{3}{*}{4-CLIQ} & 20 & 81 & 68 & Time Over & 100 & 100 & - \\ \cline{2-8}  & 50 & 32 & 29.1 & Time Over & 100 & 99.5 & - \\ \cline{2-8}  & 80 & 15.1 & 13.9 & 45.1 & 100 & 100 & 90 \\ \hline \multirow{3}{*}{5-PATH} & 20 & 58.4 & 48.5 & Time Over & 100 & 99.2 & - \\ \cline{2-8}  & 50 & 6.1 & 4.5 & 21.4 & 100 & 99.6 & 99.8 \\ \cline{2-8}  & 80 & 5.5 & 4.3 & 24.8 & 100 & 100 & 99.4 \\ \hline \multirow{3}{*}{8-TREE} & 20 & 81.1 & 72.7 & Time Over & 100 & 99.2 & - \\ \cline{2-8}  & 50 & 12.4 & 10.4 & Time Over & 100 & 99.7 & - \\ \cline{2-8}  & 80 & 13.1 & 11 & Time Over & 100 & 100 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Synthetic graph experiment results. Table 3 presents some real-world datasets used in the experiment. These datasets are frequently used in applications, such as graph pattern mining. Additionally, the table includes the labels used for the datasets in subsequent experiments, the number of vertices and edges, the description of the dataset contents, and their size on disk. We evaluate the performance of the proposed scheme on five real-world graph stream datasets from various domains, as summarized in Table 3 (see Appendix A for the source of each dataset). The datasets are chosen not only because they are widely used benchmarks in graph mining research, but also due to their diversity in terms of graph size, density, and temporal characteristics. Specifically, DBLP represents a co-authorship network exhibiting steady growth over time, while YouTube and Skitter contain more dynamic and bursty interactions. LiveJournal and NBER capture large-scale social and citation networks, respectively, allowing us to test the scalability of the proposed scheme. By covering a broad spectrum of graph streams, we aim to provide the comprehensive performance evaluations of the proposed scheme's effectiveness and robustness. Table 4 presents the average number of patterns, average processing time, and average compression rate as a function of the batch size. Owing to the characteristics of stream-based pattern mining techniques, as the batch size increases, both the time taken to process it and amount of memory required increase. This feature means that depending on the graph compression application, it may be necessary to choose an appropriate batch size. In this study, the batch size was fixed at 300 to satisfy the size of the pattern dictionary. This allowed for a stable measurement of the performance of the proposed scheme. Figure 8 shows the time spent on each process in the overall graph compression process as a function of the pattern dictionary size. Specifically, Figure 8a,b show the performance evaluation results of the proposed scheme and one of the existing schemes, namely GraphZip, respectively. It was observed that the proposed scheme takes longer in this process owing to its more complex policy for scoring complex graph patterns. In pattern-based compression methods, the process of finding patterns occupies most of the time; the proposed scheme generally spends more than 10% of the time taken for managing patterns, while GraphZip spends less than 5%.