Paper: Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks (ICLR-2024)

Reviewer comment (Writing):
The proposed metrics are not easy to understand until reading the algorithm in appendix.

Author response:
Thanks for the comment. To address the reviewer's concern, we have split the method section into two subsections.  We also include more discussions in the related work part. 

The current organization of the paper is as follows:
- Section 2: Provides brief formulation of the graph and node classification tasks. 
- Section 2.1: Provides an information theoretic quantification of explainability of tasks and classifiers (Definitions 2 and 3). 
- Section 3.1: Shows that the explainability of a classification task is equivalent to explainability of a `good' classifier for that task (Theorem 1). This is a fundamental question and the answer allows us to conclude that since the tasks considered in simulations of Section 5 are explainable, "good" classifiers for those tasks should also be explainable, hence they should yield high fidelity values if the measure used to evaluate is well-behaved. 
- Section 3.2: Argues that while the information theoretic measures of Section 2.1 provide a theoretical foundation, and allow us to answer the question posed in Section 3.1 as discussed above, they are not helpful in practice due to high sample complexity in their estimation e.g., [1]. This leads us to define surrogate fidelity measures which are empirically estimatable (Definitions 4 and 5). 
- Section 3.2.1: Considers a previously studied surrogate fidelity measure ($Fid_\Delta$ considered in GraphXAI) and shows that it is well-behaved when applied to deterministic classification tasks. However, we argue that it suffers from OOD issues and is not generally well-behaved for non-deterministic tasks. 
- Section 3.2.2: Introduces a new surrogate fidelity measure which resolves the OOD problem in the original GraphXAI measure (Proposition 3).
- Section 5: The newly introduced surrogate fidelity measure is applied to various synthetic and real-world datasets in Section 5, and it is shown that it improves upon the GraphXAI surrogate fidelity measures, as predicted by our theoretical derivations. 

[1] McAllester, David, and Karl Stratos. "Formal limitations on the measurement of mutual information." International Conference on Artificial Intelligence and Statistics. PMLR, 2020.

Reviewer comment (Evaluation):
It is unclear why we need to consider the rate of convergence in fidelity measurements.

Author response:
The rate of convergence indicates whether the fidelity measure can be accurately evaluated/estimated using a limited training set, with a faster rate of convergence indicating that the evaluation can be performed accurately on a smaller training set. 

To elaborate, note that the parametrization of explainability provided in Definitions 2 and 3 captures the intuitive notion of explainability. However, as observed in prior literature on GraphXAI, these mutual-information-based measures, whose evaluation requires estimating a mutual-information term, cannot be evaluated accurately using a limited training set (e.g., [1]). Consequently, prior works have considered the $Fid_{\Delta}$ measure as a measure of explainability as discussed in Section 3.2. The $Fid_{\Delta}$ measure, has rate of convergence $\frac{1}{2}$, and hence can be estimated using a limited, but large enough training set; however, it suffers from the OOD issue. We propose a new set of evaluation measures, which also have rate of convergence $\frac{1}{2}$ and can be evaluated using a limited training set, but which do not suffer from the OOD issue.

Reviewer comment (Theory):
On Page 5, the authors claims that the convergence ratio of fidelity is 1/2. What does that mean in practice? Does the proposed metric provide better convergence rate?

Author response:
The significance of showing that the rate of convergence of the proposed measures is  $\frac{1}{2}$ is that this is the same rate of convergence achieved by the previously studied $Fid_{\Delta}$ measure. So, we can claim that while we resolve the OOD issue to some extent, we are not increasing the necessary size of the training set for evaluating the fidelity metrics by proposing the modified measures. To summarize, we show that we achieve the same rate of convergence as the previous fidelity measure while improving in the sense of OOD.

Reviewer comment (Experiments):
The paper focus on graph classification problem. But in the experiment, there are two node classification datasets, how can this method be applied for node classification task?

Author response:
As we discussed in Section 2, "In node classification tasks, each graph $G_i$ denotes a $K$-hop sub-graph centered around node $v_i$, with a GNN model $f$ trained to predict the label for node $v_i$ based on the node representation of $v_i$ learned from $G_i$." Intuitively, we transform the node classification problem into its $K$-hop sub-graph classification problem, which is a widely used technique in the literature, such as GNNExplainer and PGExplainer.

Reviewer comment (Novelty):
Is OOD problem unique to graph? Or it also affects other domains, like images and natural languages.

Author response:
A comprehensive discussion on extension of th ideas discussed in the paper to non-graphical domians is provided in Appendix D in the revised manuscript. Additional explanation in response to the reviewer's comment is provided below. 

The underlying ideas considered in this paper, such as the ones related to the OOD issue, may be relevant to non-graphical tasks. However, the application of the specific techniques and arguments provided here to more general domains is non-trivial, and is an interesting avenue for future research. Particularly, Definitions 2 and 3, which form the foundation of our work, and provide a rigorous information theoretic definition of explainability in graphs, are based on the notion "sub-graph explanations". To extend the observations of the paper to non-graphical domains, one needs to provide analogous definitions which are not based on the notion of sub-graph explanations. 

To elaborate further and provide additional explanation, let us consider an image classification task, where the objective is to detect the presence or absence of an object, e.g., a ball, in the input image. Consider a generic explanation method used in such image classification scenarios, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) methods. Both methods can be used to generate a "heatmap" signifying the influence of each of the pixels in generating the classification output. The heatmap can be used to choose the most important "k" pixels in the image, similar to how the most important edges of a graph are chosen by graph explanation methods considered in this work. In this case, one can take the most important "k" pixels as the "sub-image" and use padding to turn it into a valid input to the classifier. Under this procedure, it should be expected that that the OOD issue would manifest. However, this definition of a "sub-image explanation" is fragile and would not be robust to affine transformations such as rotation, scaling, and shifts. So, to provide analogous analysis as those provided in our work, one needs to develop more robust notions of "sub-image explanations". This is an interesting avenue for future research and the authors will work on extending their results to non-graphical domains in the future.

Reviewer comment (Theory):
Are there computational complexities or scalability concerns with the new metrics, especially when dealing with large real-world graphs?

Author response:
Thanks for the comment. Our method is efficient because we don't re-train the classification model.  Since we need sampling to calculate the proposed fidelity measurements, we admit that the proposed method is still slower the original fidelity computation, when applied to the same finite training set. However, as we analyzed in Figure 2,  our method can achieve consistent results with a small number of samples.

Furthermore, we argue in Section 3.2 that the rate of convergence in computing our proposed metric is $\frac{1}{2}$, similar to that of the $Fid_{\Delta}$ metric studied in prior works. So, in the asymptotic regime (with vanishing estimation errors), empirical estimation of the new fidelity metric would require the training sets of similar size, and same computational complexity as that of  $Fid_{\Delta}$ .

Reviewer comment (Writing):
Minor typos.

Author response:
We also take the opportunity to fix typos.  Thank you for recognizing the significance of our paper.

Reviewer comment (Writing):
In the introduction, "nodes presenting atoms" should likely be "nodes representing atoms".

Author response:
We also take the opportunity to fix typos.  Thank you for recognizing the significance of our paper.

Reviewer comment (Writing):
In the introduction, "subgraph of the input which satisfies two conditions" should be "subgraph of the input that satisfies two conditions".

Author response:
We also take the opportunity to fix typos.  Thank you for recognizing the significance of our paper.

Reviewer comment (Writing):
In Section 3, "low error probability" should be "a low error probability", "with error close" -> "with an error close", " has Bayes error rate" -> " has a Bayes error rate".

Author response:
We also take the opportunity to fix typos.  Thank you for recognizing the significance of our paper.

Reviewer comment (Evaluation):
Why the proposed method fails with GIN on Tree-grid?

Author response:
> Q1 Why the proposed method fails with GIN on Tree-grid?

As we discussed on Page 9, we observed that all fidelity measurements fail to evaluate the GIN classifier on the Tree-Grid dataset. The potential reason is that GIN was designed for graph classification and its generalization performance on node classification is limited in the Tree-Grid dataset.

Reviewer comment (Experiments):
How to select $alpha_1$ and $alpha_2$?

Author response:
>2 How to select $\alpha_1$ and $\alpha_2$?

$\alpha_1$ is the parameter in $Fid_+$, $\alpha_2$ is the parameter in $Fid_-$. Both values are in the range of $[0,1]$. As we discussed on Page 6, when $\alpha_1=1$ and $\alpha_2=0$, the proposed fidelity measurements degenerate to the original $Fid_+$ and $Fid_-$. Our theoretical analysis suggests that we choose a small value for $\alpha_1$ and a large value for $\alpha_2$. We adopt $\alpha_1=0.1$ and $\alpha_2=0.9$ in our experiments, which achieves consistent performance.  We also conduct parameter sensitivity in Figure 1, which further verifies our selection of $\alpha_1$ and $\alpha_2$.

Reviewer comment (Theory):
What are the authors’ thoughts on how the nature of explanations should be? I think intuition says that explanation subgraphs should indeed be OOD w.r.t. the data distribution, after all, we’re looking for *sub*graphs that stand out (can be predictive on their own). Do authors generally think that the field should be moving to finding IID explanation subgraphs (if this is possible)?

Author response:
We thank the reviewer for the interesting discussion regarding the nature of explanations. In the literature of Graph explainability, intuitively, an explanation is defined as a (small) subgraph that dominates the prediction. Building on this intuitive notion, in Definitions 2 and 3 in the manuscript, we have parametrized explainability using the following two quantities: (i) the explanation subgraph  $g\_{exp}(\mathcal{V}\_{exp}, \mathcal{E}\_{exp})$ is an (almost) sufficient statistic of $\overline{G}$ with respect to the true label $Y$, i.e., $I(Y; \overline{G}| {1}_{\Psi(\overline{G})})\leq \kappa$ for a small $\kappa>0$, and (ii) the explanation has a "small" number of edges on average $\mathbb{E}\_G(|\mathcal{E}\_{exp}|)\leq s$ 
for a small $s\in \mathbb{N}$. As the reviewer has correctly pointed out, in most applications of interest, the second assumption renders the explanation OOD with respect to the actual data distribution, as the actual graph $\overline{G}$ is usually large, and has more than $s$ edges. 
In our view, condition (ii) is an essential component in defining explainability, as considered in the prior literature. The reviewer's suggestion, of moving towards a new notion of explainability is an interesting avenue for future research. To our understanding "IID explanations" in this context refer to explanations that are not out of distribution with respect to the original input distribution, e.g., graphs of similar size as the input.  In this case, one can view the explanation task as detecting or generating prototypes [1], which generates an in-distributed graph or selects a representative input as the explanation. We agree that finding such in-distributed explanation subgraphs is also important and promising, especially in model-level (class-level) explanations.  However, it should be noted that such a definition of explanation deviates from the current standard notion and would be an interesting avenue for future research. The authors would be very interested in exploring other notions of "IID explanations" that the reviewer may have had in consideration.

[1] Shin, Yong-Min, Sun-Woo Kim, and Won-Yong Shin. "PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks." arXiv preprint arXiv:2210.17159 (2022).

Reviewer comment (Evaluation):
It appears that the OOD aspect is addressed in the proposed fidelity metrics. However, it seems to me that the sampling when computing fidelity metrics means that these metrics are not actually measuring fidelity of the actual explanation subgraph (but down/up-sampled versions of it). Why/how can we say that these fidelity metrics actually correspond to the explanation subgraphs themselves when these metrics measure fidelity *not* exactly for the explanation subgraphs?

Author response:
We thank the reviewer for the insightful question. We provide an intuitive analysis here. Our response consists of two parts, firstly, we provide an argument to show that the fidelity measures introduced in this work in Equations (5) and (6) are in agreement with the notion of explainability considered in the literature under some generic assumptions, secondly, we provide an argument as to why by looking at the randomly and uniformly down/up-sampled input graphs, we can evaluate the suitability of the explanation subgraphs. 

(To be continued)

We base our response on the following three facts and intuitive assumptions:

Fact 1: For a good classifier (with low error probability), the output must be correct for most "typical" inputs, where by "typical" we loosely mean inputs that are not extremely improbable.

Fact 2: For most "typical" inputs, slight perturbations of those inputs are also "typical", e.g., if the graph is randomly and uniformly down/up-sampled by a small fraction of edges, it remains typical with high probability. 

Fact 3: For a good explanation, perturbing non-explanation edges in the input graph does not alter the true label, whereas perturbing the explanation edges would potentially alter the true label.

Fact (3) holds since as mentioned in the response to Comment 1, the notion of explainability, studied in the GraphXAI literature, considers a subgraph as an explanation if it dominates the prediction. 

Let us assume Facts (1)-(3), and let $g_{exp}$ be a good explanation for $\overline{G}$. Furthermore,  let $\overline{G}'$ be  a slight perturbation of $\overline{G}$ containing $g_{exp}$ in the up-sampled case ($Fid_{\alpha_2,-}$), and containing a sampled version of $g_{exp}$ in the down-sampled case ($Fid_{\alpha_1,+}$). 

Let us first focus on the up-sampled case ($Fid_{\alpha_2,-}$). In this case, the true label of $\overline{G}'$ is the same as that of $\overline{G}$ from Fact (3) , additionally, from Fact (2), we conclude that $\overline{G}'$ is typical if $\overline{G}$ is typical, and from Fact (1), since $\overline{G}'$ is typical, a good model must output the correct label for $\overline{G}'$ with high probability which is the same label as that of $\overline{G}$. Hence yielding a small $Fid_{\alpha_2,-}$.

On the other hand, in the  down-sampled case ($Fid_{\alpha_1,+}$),   the true label of $\overline{G}'$ is potentially different from that of $\overline{G}$ from Fact (3), additionally,  from Fact (2), $\overline{G}'$ is typical if $\overline{G}$ is typical, and from Fact (1), since $\overline{G}'$ is typical, a good model must output the correct label for $\overline{G}'$ with high probability which is potentially different from that of $\overline{G}$. Hence yielding a large $Fid_{\alpha_1,+}$.

We conclude that for good explanations, $Fid_{\alpha_1,\alpha_2,\Delta}= Fid_{\alpha_1,+}-Fid_{\alpha_2,-}$ must be large, as long as $1-\alpha_2$, (intuitively, the perturbation ratio) is small and $\alpha_1$ is small.  

On the other hand, if $Fid_{\alpha_1,\alpha_2,\Delta}$ is not large for some small $1-\alpha_2$ and $\alpha_1$ and a given explanation subgraph, that means that for a non-negligible fraction of typical inputs, slight perturbations of edges not in the subgraph lead to different output predictions (so that $Fid_{\alpha_2,-}$ is large), and/or slight perturbations of edges in the subgraph does not alter the output (so that $Fid_{\alpha_1,+}$ is small). From Facts (1)-(3) one can conclude that the subgraph is  not a good explanation subgraph, given that the prediction model is a good model. 

Consequently, we conclude that given a good model, the explanation is good if and only if $Fid_{\alpha_1,\alpha_2,\Delta}$ is large. This confirms the well-behavedness of the measures introduced in the paper. 

Now we return to the question posed by the reviewer, which is, why is it that although the fidelity measures defined in Equations (5) and (6) only consider down/up-sampled input graphs, they are able to evaluate the suitability of the actual explanation graph as argued above. 

The reason lies in the application of Facts (2) and (3) in our argument above. Fact (2) is based on the assumption that the down/up-sampling operations (perturbations) are chosen randomly and uniformly. Note that in most applications of interest, it would be possible to choose a specific slight (adversarial) perturbation of a typical input to produce a non-typical input. This has been a topic of interest in the study of adversarial ML. However, by forcing a random and uniform perturbation, we ensure that the perturbed input is typical with high probability. Fact (3) is based on the assumption that the perturbation leaves the explanation graph unchanged in the case of $Fid_{\alpha_2,-}$, so that the prediction output is the same as that of the original graph, whereas it changes the explanation graph in the case of $Fid_{\alpha_1,+}$, so that the predication output is potentially different from that of the original graph.

Reviewer comment (Evaluation):
A different direction could be to accept that predictions for OOD subgraphs are not as accurate and compute fidelity metrics and compare explanations for subgraphs with the same sparsity level, as is done in https://proceedings.neurips.cc/paper/2021/file/2c8c3a57383c63caef6724343eb62257-Paper.pdf What are the authors’ thoughts on this?

Author response:
We thank the reviewer for bringing this important paper to our attention. We have added this reference when introducing $Fid_+$ in the introduction. The fidelity metric in the mentioned paper is similar to the $Fid_{\alpha_1,+}$ measure in Equation (5), and the previously studied $Fid_{+}$ measure discussed in Section 3.2.1 of the revised manuscript. As we discuss in the introduction, ``$Fid_+$ is defined as the difference in accuracy (or predicted probability) between the original predictions and the new predictions after masking out important input subgraphs''.  

Let use denote the original graph by $\overline{G}$, the explanation subgraph by $\Psi(\overline{G})$, and the GNN model by $f(\cdot)$. Then, $Fid_+$ measures the difference between $f(\overline{G})$ and $f(\overline{G}-\Psi(\overline{G}))$. We note that this computation is also affected by the OOD of $\overline{G}-\Psi(\overline{G})$. That is, the prediction of $f(G-\Psi(G))$ is not accurate if $\overline{G}-\Psi(\overline{G})$ is OOD. To elaborate, by restricting attention to $Fid_+$, the OOD issue of  $\Psi(\overline{G})$ does not manifest since it is not used as input to the classifier in computing $Fid_{+}$. However, it should be noted that $\overline{G}-\Psi(\overline{G})$ can still have OOD issues. The fidelity measures introduced in this paper resolve both of these OOD issues by introducing the sampling variables $\alpha_1,\alpha_2$.

Reviewer comment (Theory):
The authors have done a great job at studying how fidelity metrics are not well behaved for a wide range of scenarios, which is a crucial question in GraphXAI. I, however, have some questions regarding their solution to the OOD issue of explanation subgraphs, specifically, my question 2. If this (and other concerns) are addressed, I am open to updating my score.

Author response:
We thank the reviewer for the insightful question. We provide an intuitive analysis here. Our response consists of two parts, firstly, we provide an argument to show that the fidelity measures introduced in this work in Equations (5) and (6) are in agreement with the notion of explainability considered in the literature under some generic assumptions, secondly, we provide an argument as to why by looking at the randomly and uniformly down/up-sampled input graphs, we can evaluate the suitability of the explanation subgraphs. 

(To be continued)

We base our response on the following three facts and intuitive assumptions:

Fact 1: For a good classifier (with low error probability), the output must be correct for most "typical" inputs, where by "typical" we loosely mean inputs that are not extremely improbable.

Fact 2: For most "typical" inputs, slight perturbations of those inputs are also "typical", e.g., if the graph is randomly and uniformly down/up-sampled by a small fraction of edges, it remains typical with high probability. 

Fact 3: For a good explanation, perturbing non-explanation edges in the input graph does not alter the true label, whereas perturbing the explanation edges would potentially alter the true label.

Fact (3) holds since as mentioned in the response to Comment 1, the notion of explainability, studied in the GraphXAI literature, considers a subgraph as an explanation if it dominates the prediction. 

Let us assume Facts (1)-(3), and let $g_{exp}$ be a good explanation for $\overline{G}$. Furthermore,  let $\overline{G}'$ be  a slight perturbation of $\overline{G}$ containing $g_{exp}$ in the up-sampled case ($Fid_{\alpha_2,-}$), and containing a sampled version of $g_{exp}$ in the down-sampled case ($Fid_{\alpha_1,+}$). 

Let us first focus on the up-sampled case ($Fid_{\alpha_2,-}$). In this case, the true label of $\overline{G}'$ is the same as that of $\overline{G}$ from Fact (3) , additionally, from Fact (2), we conclude that $\overline{G}'$ is typical if $\overline{G}$ is typical, and from Fact (1), since $\overline{G}'$ is typical, a good model must output the correct label for $\overline{G}'$ with high probability which is the same label as that of $\overline{G}$. Hence yielding a small $Fid_{\alpha_2,-}$.

On the other hand, in the  down-sampled case ($Fid_{\alpha_1,+}$),   the true label of $\overline{G}'$ is potentially different from that of $\overline{G}$ from Fact (3), additionally,  from Fact (2), $\overline{G}'$ is typical if $\overline{G}$ is typical, and from Fact (1), since $\overline{G}'$ is typical, a good model must output the correct label for $\overline{G}'$ with high probability which is potentially different from that of $\overline{G}$. Hence yielding a large $Fid_{\alpha_1,+}$.

We conclude that for good explanations, $Fid_{\alpha_1,\alpha_2,\Delta}= Fid_{\alpha_1,+}-Fid_{\alpha_2,-}$ must be large, as long as $1-\alpha_2$, (intuitively, the perturbation ratio) is small and $\alpha_1$ is small.  

On the other hand, if $Fid_{\alpha_1,\alpha_2,\Delta}$ is not large for some small $1-\alpha_2$ and $\alpha_1$ and a given explanation subgraph, that means that for a non-negligible fraction of typical inputs, slight perturbations of edges not in the subgraph lead to different output predictions (so that $Fid_{\alpha_2,-}$ is large), and/or slight perturbations of edges in the subgraph does not alter the output (so that $Fid_{\alpha_1,+}$ is small). From Facts (1)-(3) one can conclude that the subgraph is  not a good explanation subgraph, given that the prediction model is a good model. 

Consequently, we conclude that given a good model, the explanation is good if and only if $Fid_{\alpha_1,\alpha_2,\Delta}$ is large. This confirms the well-behavedness of the measures introduced in the paper. 

Now we return to the question posed by the reviewer, which is, why is it that although the fidelity measures defined in Equations (5) and (6) only consider down/up-sampled input graphs, they are able to evaluate the suitability of the actual explanation graph as argued above. 

The reason lies in the application of Facts (2) and (3) in our argument above. Fact (2) is based on the assumption that the down/up-sampling operations (perturbations) are chosen randomly and uniformly. Note that in most applications of interest, it would be possible to choose a specific slight (adversarial) perturbation of a typical input to produce a non-typical input. This has been a topic of interest in the study of adversarial ML. However, by forcing a random and uniform perturbation, we ensure that the perturbed input is typical with high probability. Fact (3) is based on the assumption that the perturbation leaves the explanation graph unchanged in the case of $Fid_{\alpha_2,-}$, so that the prediction output is the same as that of the original graph, whereas it changes the explanation graph in the case of $Fid_{\alpha_1,+}$, so that the predication output is potentially different from that of the original graph.

Reviewer comment (Theory):
Could you elaborate on why proving that the proposed fidelity metrics are monotonically increasing in p means that they are monotonically increasing with the mutual information?

Author response:
We thank the reviewer for encouraging us to provide the complete proof for the well-behavedness of $Fid_{\alpha_1,\alpha_2,\Delta}$ in Proposition 3. We have provided complete proof in Appendix A.4 in the revised manuscript. In the appendix we show that the mutual information is decreasing in p, hence Equation (3) holds, and $Fid_{\alpha_1,\alpha_2,\Delta}$ is well-behaved. It should be noted that Equation (3) defines well-behavedness as the property that the fidelity metrics be inversely related with the mutual information, e.g., in our example, the fidelity metric is increasing in $p$ and the mutual information is decreasing in $p$, so the fidelity metric is well-behaved. 

Note that our high-level objective in proving Proposition 3 is to show that the proposed fidelity metrics are well-behaved for a class of non-deterministic tasks as opposed to the previously studied $Fid_{\Delta}$ metric. We have added an explicit necessary condition in the statement of Proposition 3, which essentially states that the optimal Bayes classifier must not be too large (otherwise the task itself is not explainable as discussed in Section 3.1 and Theorem 1). The fact that the error can be non-zero proves our claim that  the proposed fidelity metrics are well-behaved in a class of non-deterministic tasks.

Should you have any further questions or require additional information, please feel free to contact us.

Reviewer comment (Presentation):
The paper is poorly structured. The theoretical analysis took up too much space, and many of the derivations can be moved into the appendix. In the meantime, the discussion for the motivation of Fidelity measures is too limited. Experimental results are also too limited.

Author response:
Dear reviewer dokf, 
thank you for taking the time to review our work and providing feedback. In the following, we aim to address your questions and concerns.

> W1. The paper is poorly structured. The theoretical analysis took up too much space, and many of the derivations can be moved into the appendix. In the meantime, the discussion for the motivation of Fidelity measures is too limited. Experimental results are also too limited.

We thank the reviewer for the comment. We have made the following modifications to improve the readability and the overall organization of the paper:

  - Added a descriptive title to each of the Proposition and Theorem statements in the paper, including Theorem 1, Proposition 1, Proposition 2, and Proposition 3. 
  - Separated Section 3 into several subsections, and modified the subsection titles for Sections 2 and 3 to clearly reflect the contents of each subsection.
  - Add more discussion for the motivation of Fidelity measures in the introduction (para 2 and 3).
  - Add more discussion of differences and relationships to the existing explainable GNN methods in
    related work

Please note that we have not included any theoretical derivations in the body of the original paper, and apart from the necessary definitions, problem statements, and proposition and theorem statements, all of the arguments and proofs are provided in the respective appendices. 

Through this paper, we have two main claims:

1) Existing fidelity measures are not well-behaved in a wide set of scenarios due to the OOD nature of subgraphs.
2) Our proposed fidelity measures, which are a natural result of our theoretical analysis and derivations, are more robust to OOD and are aligned well with gold-standard metrics.

To demonstrate these claims, the current organization of the paper is:
- Section 2: Provides brief formulation of graph and node classification tasks. 
- Section 2.1: Provides an information theoretic quantification of the explainability of tasks and classifiers (Definitions 2 and 3). 
- Section 3.1: Shows that the explainability of a classification task is equivalent to explainability of a "good" classifier for that task (Theorem 1). This is a fundamental question and the answer allows us to conclude that since the tasks considered in simulations of Section 5 are explainable, "good" classifiers for those tasks should also be explainable, hence they should yield good fidelity results if the measure used to evaluate is well-behaved. 
- Section 3.2: Argues that while the information theoretic measures of Section 2.1 provide a theoretical foundation, and allow us to answer the question posed in Section 3.1 as discussed above, they are not helpful in practice due to high sample complexity in their estimation e.g., [1]. This leads us to define surrogate fidelity measures which are empirically estimatable (Definitions 4 and 5). 
- Section 3.2.1: Considers a family of previously studied surrogate fidelity measures ($Fid_+, Fid_-, Fid_\Delta$) and shows that they are well-behaved when applied to deterministic classification tasks. However, we argue that they suffer from OOD issues and are not generally well-behaved for non-deterministic tasks. 
- Section 3.2.2: Introduces a new surrogate fidelity measure that resolves the OOD problem in the original measures (Proposition 3).
- Section 4: introduce related works on explainable GNNs and discuss the relationship and differences to existing works.
- Section 5: Empirically verify our claims on four benchmark datasets with two representative GNN models. 

To empirically verify our claims, extensive experiments are conducted in Section 5 and Appendix F:
 - Quantitatively evaluate the proposed Fidelity metrics by comparing them to the gold standard on four benchmark datasets with two representative GNN models.
 -  The proposed metrics have three hyper-parameters, namely $\alpha_1, \alpha_2$ and number of samples $M$.  We conduct parameter studies in Section 5.2 and F.3, respectively.  We also provide guidance on how to select reasonable hyper-parameters for real-life usage. 
 -  have added distribution analysis in Sec. F.2
 -  Compare the running time of the proposed metrics with the original ones in Section F.3
 -  Provide detailed fidelity scores with heat maps in Section F.4 to further verify the advantage of the proposed metrics
 -  We have added case studies on two graph classification datasets in Section F.5 for a better understanding
 -  Evaluate accuracy-based variants of $Fid_+$, $Fid_-$, and $Fid_\Delta$ in Section F.6 

[1] McAllester, David, and Karl Stratos. "Formal limitations on the measurement of mutual information." International Conference on Artificial Intelligence and Statistics. PMLR, 2020.

We hope that the aforementioned modifications and explanation satisfactorily address the reviewer's concerns regarding the organization of the paper. We believe experiments are comprehensive to verify our main claims.