## 4 Simulation Experiments
### Sensitivity of Parameters  
In this section, we analyze the sensitivity of parameter specifications in each algorithm from the relationships between parameter values and classification accuracy. The parameters of each algorithm are summarized in Table 3. Figures 7-9 show the effects of the parameter settings on accuracy for ASC, FTCAC, and GSOINN+, respectively. In the case  
Figure 6: Critical difference diagram of classification tasks.  
of ASC, accuracy varies depending on the combination of parameters for several datasets (Hard Distribution, Jain, ALLAML, Seeds, Sonar, and TOX171). This indicates that high accuracy cannot be maintained without a careful specification of parameters. With respect to FCTAC, high accuracy can be achieved for most datasets by choosing a large \(\lambda\) value (e.g., \(\lambda>350\)); therefore, the parameter sensitivity of FTCAC is low for a certain range of parameters. One problem of FTCAC is that the algorithm cannot build a predictive model for TOX171 with any parameter setting. GSOINN+ shows a similar trend to ASC, i.e., a careful specification of parameters is required to achieve high accuracy on some datasets.  
Figures 10-12 show effects of the parameter settings on accuracy for CAEAC, CAEAC-I, and CAEAC-C, respectively. CAEAC and its variants have two parameters: \(\lambda\) is the predefined interval for computing \(\sigma\) and deleting an isolated node, and \(a_{\text{max}}\) is the predefined threshold of the age of edges. Comparing ASC and GSOINN+ with CAEAC, CAEAC shows less variance in accuracy regardless of parameter settings. CAEAC-I and CAEAC-C also show the same trend as CAEAC. This advantage is also shown in the parameter values determined by the grid search summarized in Table 4. The parameters of ASC, FTCAC, and GSOINN+ vary widely from dataset to dataset. On the other hand, the parameters of CAEAC, CAEAC-I, and CAEAC-C show less variance across datasets than in the case of the comparison algorithms.  
From the above observations, the sensitivity of the parameters of FTCAC, CAEAC, CAEAC-I, and CAEAC-C are lower than ASC and GSOINN+. Moreover, it can be considered that FTCAE, CAEAC, CAEAC-I, and CAEAC-C can utilize the common parameter setting in a wide variety of datasets without the difficulty of parameter specification.  
Figure 7: Effects of the parameter specifications of ASC on accuracy. (**a**) Aggregation. (**b**) Compound. (**c**) Hard Distribution. (**d**) Jain. (**e**) Pathbased. (**f**) ALLAML. (**g**) COIL20. (**h**) Iris. (**i**) Isolet. (**j**) OptDigits. (**k**) Seeds. (**l**) Semeion. (**m**) Sonar. (**n**) TOX171.  
Figure 8: Effects of the parameter specifications of FTCAC on accuracy (TOX171 is N/A). (**a**) Aggregation. (**b**) Compound. (**c**) Hard Distribution. (**d**) Jain. (**e**) Pathbased. (**f**) ALLAML. (**g**) COIL20. (**h**) Iris. (**i**) Isolet. (**j**) OptDigits. (**k**) Seeds. (**l**) Semeion. (**m**) Sonar.  
Figure 9: Effects of the parameter specifications of GSOINN+ on accuracy. (**a**) Aggregation. (**b**) Compound. (**c**) Hard Distribution. (**d**) Jain. (**e**) Pathbased. (**f**) ALLAML. (**g**) COIL20. (**h**) Iris. (**i**) Isolet. (**j**) OptDigits. (**k**) Seeds. (**l**) Semeion. (**m**) Sonar. (**m**) Tox171.  
Figure 11: Effects of the parameter specifications of CAEAC-I on accuracy. (**a**) Aggregation. (**b**) Compound. (**c**) Hard Distribution. (**d**) Jain. (**e**) Pathbased. (**f**) ALLAML. (**g**) COIL20. (**h**) Iris. (**i**) Isolet. (**j**) OptDigits. (**k**) Seeds. (**l**) Semeion. (**m**) Sonar. (**n**) TOX171.  
Figure 10: Effects of the parameter specifications of CAEAC on accuracy. (**a**) Aggregation. (**b**) Compound. (**c**) Hard Distribution. (**d**) Jain. (**e**) Pathbased. (**f**) ALLAML. (**g**) COIL20. (**h**) Iris. (**i**) Isolet. (**j**) OptDigits. (**k**) Seeds. (**l**) Semeion. (**m**) Sonar. (**n**) TOX171.