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@ -10,25 +10,17 @@ Although the sampling step generates 30 million subgraphs for 50 nodes, it remai
Figure 9 demonstrates that sampling is highly efficient and scales effectively up to 64 GPUs. A minor slowdown is observed at 128 GPUs, which can be attributed to CUDA kernel overhead. Figure 9 demonstrates that sampling is highly efficient and scales effectively up to 64 GPUs. A minor slowdown is observed at 128 GPUs, which can be attributed to CUDA kernel overhead.
![Scalability of sampling on the Reddit dataset. Total sampling time for explaining 50 nodes.](figures/Reddit_Sampling_Scalability.pdf) ![Scalability of sampling on the Reddit dataset. Total sampling time for explaining 50 nodes.](Reddit_Sampling_Scalability.png)
*Figure 9: Scalability of sampling on the Reddit dataset. Total sampling time for explaining 50 nodes.* *Figure 9: Scalability of sampling on the Reddit dataset. Total sampling time for explaining 50 nodes.*
## GAT Fidelity Results ## GAT Fidelity Results
We show Fidelity₊ and Fidelity₋ scores. In all cases, a 2-layer GAT is used, and the average Fidelity score is computed over 50 test nodes. Bold values indicate the best results. DistShap achieves the best or near-best performance across all settings.}
We evaluate the fidelity of different explanation methods using a 2-layer GAT, with the average Fidelity score computed over 50 test nodes. Bold values indicate the best results. DistShap achieves the best or near-best performance across all settings.
### Fidelity₊ Scores
Table 1 shows Fidelity₊ scores for the top-k most important edges (higher is better).
**Table 1: GAT Fidelity₊ results.**
### Fidelity₊
**Fidelity₊ scores for top-k most important edges (higher is better).**
| Method | Coauthor-CS (10) | Coauthor-CS (30) | Coauthor-Phy (10) | Coauthor-Phy (30) | DBLP (10) | DBLP (30) | ogbn-arxiv (10) | ogbn-arxiv (30) | ogbn-products (10) | ogbn-products (30) | Reddit (10) | Reddit (30) | | Method | Coauthor-CS (10) | Coauthor-CS (30) | Coauthor-Phy (10) | Coauthor-Phy (30) | DBLP (10) | DBLP (30) | ogbn-arxiv (10) | ogbn-arxiv (30) | ogbn-products (10) | ogbn-products (30) | Reddit (10) | Reddit (30) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| Saliency | 0.009 | 0.029 | 0.006 | 0.021 | 0.001 | 0.003 | 0.011 | 0.029 | 0.003 | 0.006 | 0.02 | 0.031 | | Saliency | 0.009 | 0.029 | 0.006 | 0.021 | 0.001 | 0.003 | 0.011 | 0.029 | 0.003 | 0.006 | 0.02 | 0.031 |
@ -41,15 +33,9 @@ Table 1 shows Fidelity₊ scores for the top-k most important edges (higher is b
| DistShap | **0.022** | 0.027 | **0.013** | 0.022 | **0.004** | **0.006** | **0.066** | **0.137** | **0.031** | 0.033 | 0.085 | 0.186 | | DistShap | **0.022** | 0.027 | **0.013** | 0.022 | **0.004** | **0.006** | **0.066** | **0.137** | **0.031** | 0.033 | 0.085 | 0.186 |
### Fidelity₋ Scores ### Fidelity₋
Table 2 shows Fidelity₋ scores across different sparsity levels (lower is better), where a sparsity of 0.6 indicates 60% of the least important edges are removed.
**Table 2: GAT Fidelity₋ results.**
**Fidelity₋ scores across different sparsity levels (lower is better), where a sparsity of 0.6 indicates 60\% of the least important edges are removed.**
| Method | Coauthor-CS (0.3) | Coauthor-CS (0.6) | Coauthor-Phy (0.3) | Coauthor-Phy (0.6) | DBLP (0.3) | DBLP (0.6) | ogbn-arxiv (0.3) | ogbn-arxiv (0.6) | ogbn-products (0.3) | ogbn-products (0.6) | Reddit (0.3) | Reddit (0.6) | | Method | Coauthor-CS (0.3) | Coauthor-CS (0.6) | Coauthor-Phy (0.3) | Coauthor-Phy (0.6) | DBLP (0.3) | DBLP (0.6) | ogbn-arxiv (0.3) | ogbn-arxiv (0.6) | ogbn-products (0.3) | ogbn-products (0.6) | Reddit (0.3) | Reddit (0.6) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| Saliency | 0.018 | 0.033 | 0.012 | 0.024 | 0.009 | 0.015 | 0.034 | 0.058 | 0.034 | 0.055 | 0.049 | 0.106 | | Saliency | 0.018 | 0.033 | 0.012 | 0.024 | 0.009 | 0.015 | 0.034 | 0.058 | 0.034 | 0.055 | 0.049 | 0.106 |
@ -64,11 +50,7 @@ Table 2 shows Fidelity₋ scores across different sparsity levels (lower is bett
## GAT Total Explanation Times ## GAT Total Explanation Times
**GAT total explanation times for 50 nodes. Training times of surrogate models are provided in parentheses. DistShap uses 8 GPUs for Coauthor and DBLP and 128 GPUs for other datasets. (Err: CUDA insufficient resources; OOM: Out Of Memory)**
Table 3 presents the total explanation times for 50 nodes. Training times of surrogate models are provided in parentheses. DistShap uses 8 GPUs for Coauthor and DBLP and 128 GPUs for other datasets. (Err: CUDA insufficient resources; OOM: Out Of Memory)
**Table 3: GAT total explanation times for 50 nodes.**
| Method | Coauthor-CS | Coauthor-Phy | DBLP | ogbn-arxiv | ogbn-products | Reddit | | Method | Coauthor-CS | Coauthor-Phy | DBLP | ogbn-arxiv | ogbn-products | Reddit |