## 4 Experiments
### Results Details  
The results of SLIC and IMGCN-LPE for the six benchmark image datasets are listed in Table 1, and the best results are marked in bold. When the baseline model is ChebNet, the performance decreases significantly when the superpixel images are supplied because the filter of ChebNet is isotropic. The concept of direction does not exist, and it cannot clearly distinguish the different graphs represented in each image. However, in the case of IMGCN-LPE, the filter has obtained orientation by continuously learning positional information in training and can understand the graph topology in a broad context so that the power of distinguishing various graph structures increases and achieves better results. The comparison of the experimental results with the related work is presented in Table 2. Compared to the previous state-of-the-art models, the IMGCN-LPE method in FashionMNIST is about 2% higher than the DISCO-GCN, about 2.5% higher than the HGNN in CIFAR10, and about 1.2% higher than the HGNN in EuroSAT. For CAR196 and CUB200-2011, IMGCN-LPE is better by about 1.9% to 2.5% compared to the baseline models. When determining the final loss function, experimentally, a higher weight for the cross-entropy loss function provides better results. Compared to using just cross-entropy, the performance improves by about 0.2% to 5.3%. The experimental results with the 0.2 weighted ArcFace loss function are presented in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Preprocess**} & \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Dataset**} \\ \cline{3-10}  & & **F-MNIST** & **CIFAR10** & **SVHN** & **CAR196** & **CUB200** & **CUB200** & **EuroSAT** \\  & & **(28 \(\times\) 28)** & **(32 \(\times\) 32)** & **(32 \(\times\) 32)** & **(224 \(\times\) 224)** & **(64 \(\times\) 64)** & **(128 \(\times\) 128)** & **(64 \(\times\) 64)** \\ \hline \multirow{8}{*}{SLIC-75} & GCN-1 Layer & 87.542 & 46.497 & 72.682 & 51.498 & 58.477 & 58.070 & 66.487 \\  & GCN-2 Layers & 89.794 & 57.219 & 73.454 & 53.842 & 62.813 & 63.466 & 67.921 \\  & GAT-1 Head & 89.688 & 57.693 & 79.211 & 58.903 & 65.813 & 67.544 & 71.608 \\  & GAT-2 Heads & 90.874 & 61.103 & 80.726 & 59.582 & 67.560 & 69.620 & 74.863 \\  & ChebNet (\(K\) = 25) & 79.167 & 33.297 & 52.045 & 27.663 & 47.093 & 45.647 & 54.236 \\ \cline{2-10}  & & & & & IMGCN-LPE & & & & \\ \cline{2-10}  & GCN-1 Layer & 89.183 & 50.563 & 74.133 & 55.712 & 61.916 & 64.866 & 65.736 \\  & GCN-2 Layers & 90.561 & 60.464 & 75.486 & 57.808 & 65.848 & 67.212 & 66.388 \\  & GAT-1 Head & 90.618 & 66.480 & 80.511 & 61.438 & 68.223 & 69.838 & 74.758 \\  & GAT-2 Heads & **92.013** & 68.632 & **81.986** & 62.729 & **70.192** & 72.101 & **76.373** \\  & ChebNet (\(K\) = 25) & 91.21 & **73.086** & 80.714 & **60.861** & 70.484 & **69.637** & 75.482 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed experimental results of baseline models (Chebyshev graph convolutional network (ChebNet) of aggregating maximum \(K\) = 25 neighbors information, graph convolutional network(GCN) with stacking single layer (1 Layer) or two layers (2 Layers), and graph attention network (GAT) with single head (1 Head) or two multi-heads attention (2 Heads)) and IMGCN-LPE applied to SLIC-75 images.