[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
Authors: Helia Ghasemi
Christina Isaicu
Jesse Wonnink
Andreas Berentzen
Venue: Accepted by TMLR ()

Abstract: This paper seeks to reproduce and extend the results of the paper “Explaining Temporal Graph Models Through an Explorer-Navigator Framework” by (Xia et al., 2023). The main contribution of the original authors is a novel explainer for temporal graph networks, the Temporal GNN Explainer (T-GNNExplainer), which finds a subset of preceding events that “explain” a prediction made by a temporal graph model. The explorer is tested on two temporal graph models that are trained on two real-world and two synthetic datasets. The explorer is evaluated using a newly proposed metric for explanatory graph models. The authors compare the performance of their explorer to three baseline explainer methods, either adapted from a GNN explainer or developed by the authors. The authors claim that T-GNNExplainer achieves superior performance compared to the baselines when evaluated with their proposed metric. This work reproduces the original experiments by using the code (with minor adjustments), model specifications, and hyperparameters provided by the original authors. To evaluate the robustness of these claims, the method was extended to one new dataset (MOOC). Results show that the T-GNNexplainer performs best on some, but not all metrics as reported in the original findings. We conclude that the main lines of this paper hold up even though all results are less pronounced than claimed. Results show that the T-GNNExplainer does not perform similarly across different T-GNN models, precise dataset specifications are needed to obtain high performance, and there are simpler, less computationally costly explainer methods (like PBONE) that could offer competitive results.

[RE] Reproducibility Study of "Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
Andreas Berentzen University of Amsterdam
Helia Ghasemi University of Amslerdam
helia. belka.ghhserm@@@atdded..........
Christina Isaicu University of Amsteriam
chrsffii.isaccnu@@tddddd......
Jesse Wonnink University of Amsteriam
Abstract
This paper seeks to reproduce and extend the results of the paper "Fxplaining Temporal Graph Models Through an Explorer-Navigator Framework" by Xia et al. (2023). The main contribution of the original authors is novel explainer for temporal graph networks, the Temporal GNN Explainer (T-GNNFxplainer), which finds subset of preceding events that "cxplain" a prediction made by a temporal graph model. The explorer is tested on two lemporal graph models that are trained on two real-world and two synthetic datasets. The explorer is evaluated using a newly proposed metric for explanatory graph models. The authors compare the performance of their explorer to three bascline explainer methods, either adapted from a GNN explainer or developed by the authors. The authors claim that T-GNNExplainer achieves superior performance compared to the baselines when evaluated with their proposed metric. This work reproduces the original experiments by using the code (with minor adjustments), model specifications, and hyperparameters provided by the original authors. To evaluate the robustness of these claims, the method was exlended to one new dataset (MOOC). Results show that the T-GNNExplainer performs best on most, but not all metrics reported the original findings. We conclude that the main lines of this paper hold up even though all results are less pronouneed than claimed. Results show that the T-GNNFxxpaainer does not perform similarly across different. T-GNN models, precise dataset specifications are needed to obtain high performance, and there simpler, less computationally costly explainer methods that could offer competitive results.
1 Introduction
Dynamic graph-structured data can be seen as sequences of events between nodes that happen over time, and exist in many applications such as social networks (Percira al. 2018p/Gelard) et al. 2021). Temporal Graph Neural Networks (T-GNNs) learn representations of these structures and make predictions on future events (Xia et al.) 2023). The rationale for how graph models arrive at these predictions is difficult to interpret (Luo et al. 2020). and this lack of human-inteligibbe explanations means these powerful tools lack the transparency crucial in establishing fairness, safety, and trust in their output (Doshi- Velez & Kim 2017)
The opacity of graph models could hinder the possibility to ensuring ethical outcomes if potential biases or discrimination arise from their predictions, especially high-stakes domains like healthcare (Burrell 2016
Graph explainers identify important subgraphs of nodes and events that were instrumental a model's prediction for target event [Luo et al. 2020). Most methods for explaining these predictions exist for Graph

---

Neural Networks (GNNs), which focus on static graphs (GNNExplainer (Ying et al. 2019
PGExplainer Luo
et al. 2020) and Sub- graphX (Yuan et al. 20201). ). These methods cannot be directly applied to explaining
T-GNNs, since the time-varying structures of dynamic graphs are not captured by these explainers (He et al. 2022
The authors of this paper propose the T-GNNExplainer, which provides an instance-level search-basod model-agnostic post-hoc explanation for predictions made by temporal graph models. "Instance-level" means explanation is provided only for one prediction instead of at global level, "search-based means the method explores subsets of possible solutions, 'model-agnostic"" means the explainer should be able to explain any temporal graph model, and 'post-hoc' means the explanation is based on the output of the model without direct access to the actual steps that occurred during model training.
This paper is structured as follows: In Section 2 we explain the scope of reproducibility, summarizing the authors' main claims and the experiments wC ran to verify their claims. In Scction 3 we provide the reader with a summary of the model proposed in the original paper and the experimental methodology used to run the experiments. In Section 4 we describe all the datasets and in Section 3 we explained our use of the hyperparameters. In Section 6 we described the experimental setup and code, where we explain the novel dataset and the computational requirements. Section 7 provides the replicated results, comparison to the original results, and results that extend beyond the original paper. We end with a discussion in Section g Beyond the references we have also have an appendix, A to B that provides further information on the specifics of the experimental setup for those looking to implement this reproducibility study, further metrics, results, and suggestions for further research.
2 Scope of Reproducibility
The main contribution of the authors is a novel explainer for T-GNNs. They claim superior performance of their explainer by evaluating performance in comparison to other basclines explainers, evaluating the efficiency of their method, and the conciseness of the output compared to the other explainers.
The first metric they use to evaluate performance is called the Area under the Sidelity-Sparsityy Curve (AUFSC). It composed of two metrics (fidelity and sparsity), each of which is used to evaluate graph explainer models (Amara et al. 2022) (Agarwal et al.) (023) (Liu al., 2021). The curve expresses the re- lationship between the fidelity (i.e. how accurately the T-GNN makes simílar prediction when it takes input only the nodes it selects as explanatory) and sparsity (i.e. how few nodes are used to give the expla- nation). The area under the curve is measure of the commutative fidelity over a set of sparsity threshold intervals. High fidelity and high sparsity result in a higher AUFSC which indicates better performance, The second metric, Best Fidelity, simply measures the highest fidelity achioved by an explainer without the sparsity limitation, averaged over all test data. The metrics are explained in further detail in Section 3.10
The main claims that are investigated in the current paper are:
Compared to the other baselines, T-GNNExplainer surpasses performance in the AUFSC and Best Fid metrics by up to ~50%.
2. The T-GNNExplainer is model agnostic in regards to the underlying predictor model.
The aim of this paper is to verify the original authors' main claim by reproducing their main findings, and testing the robustness of this claim by extending their results to a new and different kind of dataset. We perform further analysis on the explanatory graphs and the interaction between the T-GNNExplainer and the underlying model. Further goals of the paper are to increase accessibility and reproducibility of this work by providing supplementary instructions for running the code and in-depth explanations for understanding the methodology.
There are other claims made in the paper in regards to the conciseness of the generated graph and the navigators efficiency. We do not investigate these claims due to computation and time cost.
2

---

3 Methodology
The code publicly available in a zip file of supplementary materials on the OpenBeview page for this paper. The data not included in the zip file, but the top-level ReadMe in the code provides instructions for downloading the data. There some inconsistency between the nomenclature used in the paper and the corresponding terminology in the code. For the purpose of this work, we follow the terminology used in the paper.
3.1 Notation
The notation used in the paper is as follows: G = (N, S) is a temporal graph. N a list of the nodes, and S = {e1, e.....) sequence of timestamped events. Each event 6; {Mu,: Tey 4, att;) has a timestamp 4 and occurs from node Thu to node By; with the attribute att,, which is list of indefinite even length filled with feature values. G is the graph at timestamp & and R is the subset of events in graph G found by the explainer. N, is hyperparameter controlling the amount of nodes that the explanatory subgraph is allowed to have.
The function f()) represents a trained temporal graph model. [ex* is the probability prediction of the event EN that occurs when the graph G is input into the network. The output is a logit value. Ys is a value of cither or that classifies the prediction of the network for whether or an event occurs the time step k.
3.2 T-GNNExplainer
We explain the Explorer-Navigator framework in detail to give a more intuitive understanding of the model intended functioning.
A temporal graph network predicts whether a target event e& will occur or not (Rossi et al. 2020 X et al. 2020). The purpose of the T-GNNExplainer is find set of preceding events that explain why the model made this prediction.
This subset of events R² henceforth referred to the "explanatory subgraph" is found from set of candidate events. These candidate events are selected through three hyperparameters. The first hyperparameter n hops in the code) limits them spatially to be within a k-hop neighborhood of the target event (i.c., 2 hops). The second num neighbors in the code) restricts them temporally to the larget event (i.e., within the 10 most recent events). R² finally, is further limited by the hyperparameter Nr, which constrains the maximum number of nodes that the explanatory subgraph can have. In the code, this number is hard coded to 20. The reasoning is that if the subgraph is too large e.g. if | = | then the explanatory power of R becomes tautological, losing usefulness in its lack of specificity.
The explainer module is made up of two parts, the navigator and the explorer, that work together to "prune" a graph of unimportant events related to the target event ex, resulting a graph of only the most relevant events that lead to the prediction of the target event.
3.3 Navigator
The navigator is inspired by the "cxplanation network for node classification" from the paramcterized ex- plainer proposed by Luo et al. (2020). The navigator he(e,, ex) proposed in this paper is a two-layer multi-layer perceptron (MIP) that learns a correlation between two events ej (the candidate) and ek (the target).
To infer the relationship between two specific events, the features of both events are concatenated into a vector and input into the network:
Zag,ox (Xnug | | [Time((s)]latte]] Xxn,, (|atte]
3

---

X represents the node feature matrix, T-me(-) () is harmonic function that encodes the real-valued timestamp into a learnable vector adapted from (Xu et al. 2020 and || represents concalenation.
Given that the navigator is an MLP that takes as imput the concatenated features of two events to learn a relationship between them, this indicates that the network learns similarities between events. So when the explorer (explained in section 3.4 below) uses the navigator to remove "unimportant" events from the search space, the navigator selects dissimilar events to the target event.
3.4 Explorer
The explorer uses modification of Monte Carlo Tree Search (MCTS). The explorer searches tree of nodes, where each child node is subgraph of its parent node, which has one fewer event than the parent before it.
The root node is initialized as a subset of g that satisfies the constraints imposed by temporal and spatial hyperparameters.
Starting from the root node, the explorer selects and expands child nodes. It is important note that these child nodes not stored in memory. Rather, the explorer keeps track of list of previously expanded events at each node C(N"). This entire process is conducted through multiple rounds (i.e. rollouts), hyperparameter that is set to 500 in this paper.
Each rollout starts with the root node and we prune events until we reach a leaf node, which is a child that satisfies the hyperparameter criteria that it has fewer than events. In the code itself, a leaf node is defined as subgraph with event in the code, and less than 5 events in the paper.
Child nodes are selected and expanded until a leaf is reached. The search tree of all nodes is not not stored in memory across rollouts, rather cach rollout starts with the root and list of events that were expected and expanded in previous rollouts.
3.5 Node Selection
The search path is selected using the following formula:
V Eereowsy m(N',e))
cdV' ej) n(N', + A 1+n(Ni,
e" = arg max eyEC(N
(1)
Since the nodes themselves are not stored in memory, child node is "selected" by removing an optimal event e* from the current node N This equation says that from events that have already been expanded, select an event that has high average reward value from previous rounds (exploitation term) but has not been explored as much (exploration term). The X is hyperparameter set by the paper authors that controls how much exploration is encouraged.
3.6 Node Expansion
Instead of generating and scarching a tree all possible subgraphs, the explorer uses the navigator create subgraphs that are relevant its search. The navigator finds the least important event in relation to the target/predicts event ex:
e" = arg min he(e,,ex)
(2)
CJENT/CUY
The explorer then creates new child node by removing from the current node N₁- It important note that the navigator only searches events that were not already removed in previous rollouts, so that we do not end up removing the same event in every rollout.
4

---

3.7 Reward & Backpropagation
Child nodes are selected and expanded until we reach a leaf node. Once we have reached this leaf node, reward is calculated through the negative cross-entropy loss based on the work of Farnia & Tse 20170:
min R
Σ c=0,1
1(Yx = c) log P(Ynew = c|T²)
The loss looks at how much the probability of a prediction (Yr) of the original graph (Gk) changes when the input limited to specific subgraph (R*). the probability changes lot, then that subgraph is not good explanation for the prediction made event eg by the original graph. The outcome of the loss function is seen as the reward for the back-propagation. The baok-propng step itself, consists of adding the cumulative reward value for each subgraph/nod c(Vi,,) ej), and how many times il received reward c(V". ey).
3.8 Selecting the explanatory subgraph
The esplorer-navigatee process is repeated through several rollouts to explore different possible subgraphs. The best explanation for the prediction e is the subgraph that has the highest cumulative reward and has the simplest explanation according to the sparsity threshold (|R* S N, equation
3.9 Optimal explainer
The optimal explainer g" is the minimum cross-entropy averaged over all predictions for k target events
g" = arg min
L(Y of 1(Yx 0) log(1 of ((((")))x)))
(4)
Subject to R² = g(ex, g* f())) and R² C gh and R s Nr
where N, is the hyperparameter that controls the size of R...
3.10 Evaluation
The two metrics used to evaluate the performance of the explainers the Area Under the Sidelity-Sparsity Curve (AUFSC), and Best Fid (best fidelity). The formula for sparsity is given by: Sp = |R l Sparsity is ratio between the size of the explanatory subgraph and the input graph g².
The formula for Fidelity is given by: Fid(
=Y -
1(2 = 0)( - ((7+))))))
(5)
Fidclity measures the difference between the logit probability output by the temporal network f()) when the imput is the original graph 9+ and when the input is the explanatory subgraph Rt. logit score of +1 implies that the explanatory subgraph is better prediction than the original graph. The model aims to maximize the fidelity.
The AUFSC is a numerical score that measures the area under the graph for the fidelity values at sparsity intervals between and (|R*) = (g A higher value for AUFSC implies the explorer achieves high fidelity, even at low sparsity threshold.
The goal is to have high fidelity, which means that [ex] produces logit probability that ideally surpasses the original prediction [ex), while keeping the explanatory subgraph R² as simple as possible. The purpose is not to achieve the same probability output on [(R* as [((*))ee) (fidelity = 0) but rather to obtain a subset that increases the logit number for this prediction as much as possible (positive fidelity). In a way, we are picking the best events to generate this prediction which we call the "explanatory subgraph'.
The metric Best Fid simply measures highest fidelity achieved by an explainer, ignoring sparsity altogether. The only limitation the hyperparameter N, controlling the maximum amount of nodes allowed.
5

---

3.11 Baselines Other Explainers
The authors compare the performance of the T-GNNExplainer with three baseline explainer methods: PG- Explainer (Luo et al. 2020), ATTN and PBONE [iia et al 2023). In contrast to T-GNNExplainer, the baselines are not search-based methods. The original PGExplainer is a paramotrized explainer for GNNs that provides a model-level explanation over multiple event predictions. The authors adapt PGExplainer output explanations for temporal graph structures an instance-level. The two other explainers were modelled by the authors themselves. Firstly, there is ATTN which an attention-based explainer that aver- ages the weight values over all attention layers of either TGAT TGN. PBONE generates an explanation by removing or 'perturbing' one candidate event at a time to compute its importance in prediction (Xia ct al. 2023
3.12 Target models
The authors test the explainer two target models, namely TGN and TGAT. TGAT is temporal graph attention layer that is used in neural networks. This technique uses self-attention and stacks TGAT layers to recognize the node embeddings as functions of time (Xu et al. 2020). Temporal Graph Networks (TGN) is a deep learning framework where dynamic graphs are represented as sequences of timed events (Rossi ct al. 2020). TGN consists of combination of memory modules and graph-based operators. In contrast TGAT, TGN uses Multi-head Attention in their network.
4 Datasets
The dataset collection consists of bipartite graphs and unipartite graphs, all of which are directed. Bipartite graphs consist of two types of nodes (e.g. users and pages). Events in the datasets describe one type of node interacting with the other type (Chang & Tang 2014). Unipartite graphs consist of one type of node, among which events occur (Chang & Tang 2014).
The original paper experiments with two classes of datasets with very distinct characteristics. One is a bipartite graph network describing an online collaboration process with extensive feature information, and the other is a dataset generated by statistical process in small-scale graph with predefined, static event relationships.
4.1
Data format
The original paper makes some claims about the data structures that conflict with the implementation of the code. This section highlights implicit assumptions and discrepancies relevant to our discussion in Section about the precise data specifications needed to run the models.
It first important to note that every dataset used is a directed graph, the format consists of events that occur from node nu; to node nu,, There can multiple events from node nu; to node nyy (i.e. a user updates a page multiple times). The original paper states that there could be deletion or internal update events e, = {mayy null, 4, att;), however, the implementation for the T-GNNExplainer does not allow for null events, therefore there
self-updates self-deletions of nodes.
Even though the original paper claims that the T-GNNExplainer takes into account the contimous-time dynamic properties of the data (Xia et al. 2023), only the underlying predictor models do this. Analysis of the methodology and code reveals that T-GNNExplainer only looks at events ordinally, and ignores the amount of time that passes between events.
4.2 Synthetic datasets
The synthetic datasets are based on a unipartite temporal graph that contains nodes, and 4 possible odgo-events. The possible events within this graph adhere to predefined excitatory and inhibitory relations. These predefined relations are used in a multivariate Hawkes process to generate a dataset of timestamped events. Hawkes 1971 implemented in the Tick Python library (Bacry et al.] 2018 A random process is

---

used to generate noise in the form of false events. In summary, the synthetic datasets are relatively small datasets that describe the occurrence of four unique events over time.
4.3 Real-World datasets
The two real-world datasets used in the original paper, Wikipedia and Reddit (Kumar et al, 2019), are bipartite graphs that describe editing processes between users and web pages. In both datasets, this process follows roughly 1:9 ratio between web pages and users. Each event is accompanied by 172 mumeric features vector that describes the editing event.
It should be noted that although is claimed these datasets are bipartite, about 10% the connections involve nodes that are contained in both sets, this would disprove the bipartite characteristic. We have not found reason for this phenomenon in the original paper from the authors who compiled the datasets [Kumar et al. 2019
4.4
New dataset
To validate the robustness of the claims made by the original authors regarding AUFSC performance, dataset is included in the experimentation, MOOC is network representing student interaction from online course content units (Kumar et al. 2019).
MOOC exists in a desirable interposition of dataset characteristics to the original datasets. It is equal in size but has fewer features and can serve to test the generalizability of the proposed methodology on real-world temporal graph processes with limited event information.
Table 1: Dataset attributes. Where # represents 'Number", so "Number of Events' and 'Number of Fea- tures".
Name
# of Events
# of Features
Type
Time period
Time granularity
Wikipedia
157,474
172
bipartite
month
seconds seconds
Reddit
672,447
172
bipartite
month
MOOC
411,749
bipartite
-298 days
seconds
VI
10,000
unipartite
5500 seconds
<seconds
V2
10,000
unipartite
-500 seconds
seconds
All data sets have 70% / 15% / 15% train / validation / testing scherne based on timestamps for both
TGAT and TGN. The most recent 30% is used for the validation and test sets (Kumar et al. 2018
Kumar
et al. 2019).
5 Hyperparameters
The experimental setup is copied from Section 5.2 and the Appendix of the original paper (see Appendix B below for further details).
For the novel dataset, minor changes have been made. Firstly, before training the graph prediction models (TGAT and TGN) the embedding dimensionalities are adjusted to match the dimensionality of the feature information in the novel dataset.
The experiments were run on two seeds; the original seed 2020, and a new seed 2. See the discussion section for further comments about the incomplete seed set-up in the original code.
Due to the limitation of computational resources, we chose to focus our hyperparameter tuning efforts on recreating the hyperparameter analysis performed by the original authors in their Appendix Scction A.7. Our experiment is performed on the TGN model trained on the Wikipedia dataset, where X, the navigator's exploration parameter, is set 5, 10, and 100.

---

Table 2: Other hyperparameters from the paper and justifications regarding why they were excluded from further studies.
Hyperparameters omitted from tuning
Hyperparameters
Value
Explanation for omission
Graph-level explanations are not implemented by the authors but there is evidence in the code of their intention to implement this option.
Explanation level
Event
The authors performed rollout analysis in the Appendix, but we facod compute limitations This hyperparameter
Rollouts
500
hardeoded by the original authors, code refactoring out of scope for this paper. Limitation of computational resources. Values are very specific to the dataset.
N, (See Section 3.9
20
Learning rate
le-4
Time dim
172 or 4
Node Feature Dimension
172 or 4
Values are very specífic to the dataset.
Edge Feature Dimension
172 or 4
Values are very specific to the dataset.
Training epoch
10 or 100
Limitation of computational resources.
6 Experimental setup & Code
The following experiments were performed: 1) Reproduction on the original performance claims across three soeds, 2) Extension of performance evaluation to one new datascts, 3) hyperparameter tuning of X, the value responsible for the 'exploration' of the navigator.
The repository can be found here httpss//gttabb..oomccca///gggss
6.1 Reproduction
To reproduce the original results, we train the models (TGAT, TGN) on the original datasets (Wikipedia, Reddit, Synthetic V1, Synthetic V2). We then train the explainers (T-GNNExplainer, PGExplainer, ATTN, PBONE) on the trained temporal graph networks from the previous step. Finally, the performance is evaluated using the evaluation methods in the authors' original code to measure AUFSC and Best Fid scores (evaluation detailed in section 3.10
6.2 Extension with novel datasets
New datasets need to adhere to a number of constraints to train explainer models, which include data format constraints and content constraints. Before the new dataset can be used in training, it needs to be processed match the navigator feature vector as described in Section The full experimental setup of the new datasets described in Appendix B
6.3 Computational requirements
For this project, cluster with each node having 24 CPU cores (AMD EPYC 7F72) and one A100 GPU with 40GB RAM.
initial set of experiments (not included in the paper due to lack of seeding)
The updated experiments, which were run over 2 seeds and include hyperparameter tuning, were run on nodes with 4x T4 GPUs with 16GB RAM each and 64 CPUs. We ran each experiment with 8-64 CPU cores and one GPU, since the T-GNNExplainor can benefit from CPU parallelism.
Training times also tracked:
8

---

In our initial set of experiments, the average training time was hours for each model, and 26 hours for each explainer. The total number of computational hours spent were 88 hours for the training of the models and 345 hours for the rumning the explainers (including re-runs for failed runs).
For the updated experiments, the average training time was 7 hours for each model per seed, 53 hours for the T-GNNExplainer on a single CPU, and 12 hours for all other explainers combined on single CPU (per model). The computational hours required for the results presented in this research can be split up into 180 hours for training of the models, and 165 hours for running the explainers across 8 CPUs (including re-runs for failed runs). The cost of the updated experiments totaled over 6000.
We discovered that GPU resources were not optimally allocated in sections of the authors' original code, which resulted in slower computation that was performed on CPUs. We made minor adjustments to improve efficiency but did not perform a thorough analysis and refactoring the code.
7
Results
7.1 Results original paper
In this section, we report our reproduced results in Table 3 Table 4 Table and Table for the real-world and synthetic datasets for all target models. New results compared to the old results from the original paper, and Delta refers to the difference between the two. Bold values highlight the explainer with the highest value for a given metric. As far as we know, the original papers results were not run over multiple seeds.
Table 3: Best fidelity and AUFSC achieved by each explainer Wikipedia dataset, averaged over two seeds.
Wikipedia
TGAT
TGN
BestFid New
AUFSC
Best.id New
AUFSC
Old
Delta
Old
New
Delta
Old
Delta
Old
New
Delta
ATTN
0.891
0.461
-0.430
0.564
0.510
-0.054
0.479
0.034
-0.445
0.073
0.047
-0.026
PBONE
0.027
0.998
0.971
-2.227
1.035
3.262
0.296
0.111
-0.185
-0.601
0.127
0.728
PG 1.354
-0.475
-1.829
0.692
-0.420
-1.112
0.464
-0.532
-0.996
-0.231
-0.504
-0,273
T-GNN
1.836
1.236
-0.600
1.477
1.390
-0.087
0.866
0.413
-0.453
0.590
0.496
-0.094
Table 4: Best fidelity and AUESC achieved by each explainer on Reddit datasct, averaged over two seeds.
Reddit
AGAT
TGN
BestFid New
AUFSC
BestFid New
AUFSC
Old
Delta
Old
New
Delta
Old
Delta
Old
New
Delta
ATTN
0.658
-0.980
-1.638
-0.654
-0.889
-0.235
0.575
-2.223
-2.798
0.289
-2.86
-2.475
PBONE
0.167
0.725
0.558
-2.492
0.798
3.290
0.340
-0.876
-1.216
-0.256
-0.807
-0.551
PG
0.804
-0.877
-1.681
-0.369
-0.796
-0.427
0.679
-2.438
-3.117
0.020
-2.418
-2.438
T-GNN
1.518
0.561
-0.957
1.076
0.753
-0.323
1.362
-1.570
-2.932
1.113
-1.496
-2.609
Performance on the Reddit dataset is an outlier compared to other datasets. The PBONE method performs comparable to the T-GNNExplainer on both models trained on this datasct. All explainers have lower performance on this dataset for both models (i.e. the delta values are mostly negative), except for PBONE TGAT.
9

---

Table 5: Best fidelity and AUFSC achieved by each explainer on Synthetic vl dataset, averaged over two sceds.
Synthetic v1
TGAT
TGN
Fist
AUFSC
BestFid
AUFSC
Old
New
Delta
Old
New
Delta
Old
New
Delta
Old
New
Delta
ATTN
0.555
1.136
0.581
0.390
1.166
0.776
2.178
0.452
-1.726
1.624
0.477
-1.147
PBONE
0.044
1.431
1.387
-2.882
1.447
4.329
0.000
0.693
0.693
-3.311
0.721
4.032
PG T-GNN
0.476
0.634
0.158
-0.081
0.676
0.757
2.006
-0.404
-2.410
0.626
-0.337
-0.963
0.780
1.472
0.692
0.666
1.596
0.930
2.708
0.828
-1.880
2.281
0.945
-1.336
Table 6: seeds.
Best fidelity and AUFSC achieved by each explainer on Synthetic v2 dataset, averaged over two
Synthetic v2
TGAT
TGN
BestFid
AUFSC
BestFid
AUFSC
Old
New
Delta
Old
New
Delta
Old
New
Delta
Old
New
Delta
ATTN
0.605
0.859
0.254
0.291
0.874
0.583
0.988
0.217
-0.771
-0.634
0.237
0.871
PBONE
0.096
1.856
1.760
-4.771
1.901
6.672
0.320
0.811
0.491
-5.413
0.860
6,273
PG
1.329
-0.664
-1.993
-0.926
-0.641
0.285
1.012
-0.323
-1.335
-1.338
-0.328
1.010
T-GNN
1.630
2.286
0.656
1.331
2.438
1.107
4.356
1.001
-3.355
3.224
1.136
-2.088
In our reproduction of the experimentation, T--NNEExplanner largely does not achieve the same performance in the original experimentation in terms of AUFSC and best fidelity scores. On the real-world datasets, there is general trend of scoring worse in comparison to the original experimentation. PBONE is an exception to this trend, getting in close contention with T-GNNExplainer for the best performance on the Wikipedia dataset. On the Reddit dataset, which is the most 'difficult' dataset for the explainers, PBONE outperforms T-GNNExplainer across the board.
The new results broadly reflect botter performance for most explainers on both metrics for TGAT trained both synthetic datasets (vl and v2). The performance for most explainers TGN is lower however, except for the baseline explainers the AUFSC metric.
Observing the results, the salient trend is that T-GNNExplainer scores lower on all metrics in our reproduc- tion of the experimentation compared to the original, except for TGAT Irained on the synthetic datasets. Performing T-tests on all new values shows all new values are significantly different, meaning the result from our reproduction unlikely to due to chance.
The figures below offer visual representation of the AUFSC metric. T-GNNExplainer indeed achieves higher fidelity at lower sparsity values (except for TGN trained on Reddit), with PBONE performing second-best.
10

---

Averop this by aee Sarnit for Eact Modescroe Camsindee Korms Seeb
ett_eexlainer
pbone exolainer
exollinie
T-GNNExplainer
Figure Eight graphs for AUFSC curves and corresponding legend for all four explainers averaged across seeds. The legend indicates the lines for each explainer: blue, orange, green, and red, for ATTN, PBONE,
PG Explainer, and T-GNNExplainer respectively.
7.1.1 Hyperparameter Tuning
0.0
-0.5
-1.
-20
lmmbd=1
ambda=5
lmmba==100
0.0
0.4
0.8
Sparsity
Figure 2: Best fidelity across sparsity intervals on unique lambda values.
We performed tuning on the hyperparameter À to investigate its effect on the explorer in the T- GNNExplainer. A higher value for encourages more exploration in the node selection step of the explorer (see 3.5 The authors performed this analysis both models trained the Wikipedia dataset with À set to 1, 5, 10, and 100. They found that lower values for X, which preference exploitation in node selection (particularly A = 1), marginally improve performance the fidelity-sparsity curve. However, they observed that the difference in performance is insignificant.
In our reproduction, values for À set to 5, 10, and 100 all output the exact same results, while À set to 1 output marginally different results that are not noticeable in Figure
11

---

tgn mooc
tgat
0.00
-0.25
Fideityy
Fideiity
-0.50
-075
attn, explainer
pbone expllinner
-1.00
Pg. explainer
T-GNNExplainer
-1.25
0.4
0.6
0.8
1.0
0.4
0.8
1.0
Sparsity
Sparsity
Figure 3: AUFSC curves for both models trained on the Mooc dataset, and corresponding legend for all four explainers. The legend indicates the lines for each explainer: blue, orange, green and red for ATTN, PBONE, PG Explainer, and T-GNNExplainer respectively.
7.2 Results beyond original paper
In this section we report our results in Table 7 for the new real-world dataset MOOC. In Figure 3 illustrate the sidesity-sparsity curve on the new real-world dataset.
we
Table 7: Best fidelity and AUFSC achieved by each explainer on MOOC, averaged over two seeds.
MOOC
TGAT
TGN
MeanBestt Fid
AUFSC
MeanBest Fid
AUFSC
ATTN
0.077
0.091
-0.099
-0.085
PBONE
0.294
0.299
-0.097
-0.085
PG
0.047
0.057
-0.280
-0.273
T-GNN 0.339
0.369
0.172
0.201
For both models trained on MOOC, T-GNNExplainer outperforms all bascline explainers both metrics. (See Table 7. PBONE performs competitively with TGAT the prediction model, however it's inconsistent with TGN underlying model.
The variability plot in Figure 4 shows variability in performance of the T-GNNExplainer between the underlying T-GNN models. Using TGAT as an underlying model yields less variance in the outcomes over models in comparison to TGN.
12

---

Variability Comparison of TGAT and TGN
1.5
AUFSC Mean
0.5
TGAT
TGN
Figure 4: Variability comparison of TGAT and TGN. The mean is taken over all the datasets for the AUFSC metric for each target model, with its standard deviation.
Table 8: The percentage differences between T-GNNExplainer and the best baseline explainer. For example, the first number in the first column indicates that in the original results, T-GNNExplainer outperformed the second-best baseline on the Best Fid metric by 35.6%. This table shows that the difference between T- T-GNNExplainer and the second best explainer (PBONE) not pronounced our reproduced results in the original results. For the new results, the negative values indicate where T- T--NNExplaiinr performed second best to PBONE.
Percentage % difference between T-GNN and best bascline explainer on real-world datasets
Wikipedia
Reddit
MOOC
Model
Metric
Old
New
Old
New
New
Best Fid
26.64
13.91
52.85
9.63
15.68
TGAT
AUFSC
21.19
19.61
40.50
2.68
22.23
Best. Fid
67.89
32.02
66.83
44.43
59.44
TGN
AUFSC
43.41
36.95
60.19
42.78
60.28
8 Discussion
Temporal graph neural networks have the unique ability to infer and predict the developments of evolving network structures (i.e. temporal graphs), in which processes from different scientific disciplines such as ecology and social science (oottin et. al. 2012) YYu et al. 2018 can be expressed.
In our opinion, an explainer for T-GNNs bocomes truly useful when can successfully leverage the complex and dynamic inhibitory and excitatory influences among events present in real-world processes to provide end-users with computationally feasible and accurate explanations for novel events.
13

---

8.1
Main claim
Almost all of the values for the AUFSC and Best Fid metrics differ in our reproduction of the original experiment. Despite lower performance in nearly all the output values on both key metrics compared to the original results (See Tables 7
6 the reproduced experiments still largely support the claim that the T--Ninxplainer outperforms the PGExplainer and ATTN explainers. However, it does not always outperform PBONE. In the instances where T-GNNExplainer does not outperform the baselines, PBONE performs best. (Reddit for both TGAT and TGN both metrics). T-GNNExplainer performs second best in these cases.
In our analysis of the main claim that T-GNNExplainer outperforms the leading baseline by up to ~ 50% on the AUFSC metric, it is unclear how the original authors calculated this value. We calculated the percentage difference using (T-GNNN)bbet) baseline
100 and applied to the original results (See Tables These new calculations differ from the reported percentage difference in the original paper. For example, the original paper states that the T-GNNExplainer outperforms the leading baseline (PGExplainer) for TGN trained on Wikipedia on AUFSC by 86% whereas we found it outperforms by ~ 700% (See Table 8) Table show that the percentage difference is consistently lower on most comparisons for the now results comparod to the old results. This confirms that the the T-GNNExplainer does not. perform as well in the reproduced experiments as in the original results, on both the Best Fid and AUFSC metrics.
6
[best baseline]
g
8.2 MOOC dataset
Experiments on the MOOC dataset reveal very similar trends to all explainer models (See Table 7 but the variance of the results is much smaller and closer to O. The underlying T-GNN models did not score a high accuracy on MOOC compared to the other datasets ( 96% compared to 66% respectively).
Since the prediction of these T-GNN models are used as the 'ground truth" for the explainers, the explainer is dependent on the performance of the underlying predictor model. Since the explainer doesn't 't look "under the hood' at the steps that the T-GNN model takes to make prediction, and rather uses the T-GNN model to find post-hoc explanations, it follows that when the estimates are noisy, the performance of the explainer suffers.
Considering the low accuracy of both TGN and TGAT on MOOC, this could explain why the variance of the results on MOOC far smaller than on other datasets.
8.3 Model agnostic claim
Although the authors claim that T-GNNExplainer is model agnostic, it docsn't scem to stay true for these two SOTA models. Our results show that the explanatory power of T-GNNExplainer differs between the TGAT and TGN models. Figure [ shows that the T-GNNExplainer performs worse overall on the TGN model than TGAT, and and Figure shows performance on the TGN model has higher variance, so it also offers less certainty.
8.4 Reddit dataset
All explainers underperform the Reddit dataset. One possible explanation points to the size of the dataset and the method for splitting the data. Reddit is by far the biggest dataset (~ 260000 bigger than MOOC). When training the T-GNN models, the data is split temporally where the first 85% events are used for training and validation and the last 15% events are used as test data. The problem with this method is that graph data is not independent, so events earlier in time could influence events later in time, Separating the data in this way can lead long-distance predictions that are inherently more uncertain. In line with this is MOOC, which is the second biggest dataset, also resulting in low scores comparatively. More advanced temporal splitting methods could be used to obtain more valid outcomes (de Bruin et al. 2021
14

---

Another reason could be that the Reddit dataset seems to have mistakes in it. It is said to be bipar- tite dataset representing users editing pages. However there are nodes that appear in both sets, which is impossible in a bipartite dataset. This could result in worse performance as the data is not clean.
8.5 Hyperparameter Tuning
The negligible difference in performance that is measured in all intervals of the hyperparameter suggests that including the exploration term in the node selection process of the explorer is largely insignificant in comparison to the exploitation term, because the pre-trained navigator is very dominant in finding optimal subgraphs, in comparison to exploring novel options.
Given that the Monte Carlo Tree Search in the T-GNNExplorer is one of the most computationally slow and costly procedures of the model, the insigificance of the exploration term raise questions whether the explorer could benefit from other, more efficient methods of subgraph-search.
8.6 Computational efficiency
The authors discuss the computational efficiency of the T-GNNExplainer in comparison to the baseline models in their Appendix. They found that the T-GNNExplainer is significantly slower than the baseline models, none of which are search-based methods. For example, T T-GNNExplainer takes 28.24 28.2s to explain one instance in the Reddit datset trained on TGAT compared to 0.39s for PBONE (Appendix by (Xia et al. 2023)). They state that the complexity of the MCTS algorithm is O(NDC) (Appendix by (Xia et al. 2023) where is the number of rollouts, is how far each rollout expands a node, and constant for inference time. Given that in the code, the hard coded leaf nodes meant that each rollout expanded nodes all the way down to a subgraph containing event, the complexity was very high. Efficiency would likely be improved if this rollout search wasn't 't continued to the final node.
Given the extreme computational demand detailed in Section 6.3 for merely marginal improvements over simpler explainers such as РBONE, it is unclear whether the use of T-GNNExplainer can be justified de- spite its superior performance. Beyond performance, accessibility is an important factor in measuring the 'usefulness' of tools such as T-GNNExplainer, especially for teams with limited funds, time, or resources.
8.7 Discrepancy between results
Given that seeding was not properly implemented in the original study, it makes sense that none of the values in the reproduced experiments match the original results, however it is unclear why the values differ drastically. One interesting discrepancy is that in our results, PBONE either performs second best, in some cases, outperforms T-GNNExplainer. In the original results, the leading baselines are either ATTN or PGExplainer. PBONE performs best on AUFSC for both models trained on the Reddit dataset. instances where PBÔNE performs second best, its evaluation metrics are often close in value to those of T- T-GNNExplainerr
8.8 Conclusion
Since T-GNNExplainer is computationally intensive given the task (i.e. it only provides instance level ex- planations), it is not clear whether it is model-agnostic, and it requires very precise dataset configurations, it would be valuable to explore other methods to provide explanations for T-GNNs. In comparison to T- GNNExplainer, PBONE is a very simple model that is computationally much faster than T-GNNExplainer. Considering its simplicity, computational efficiency, and its ability to 'compete" with T-GNNExplainer according to our findings, refining perturbation-base. methods could be valid avenue for exploration. Justification could be found in similar methods explored by Lucic et al. (2022) where the authors use a perturbation-basel explained method GNNs to achieve strong results.
15

---

9 What was easy and what was difficult?
What was easy: The authors made their code accessible online, without which the T-GNNExplainer would not have been reproducible based on the paper alone. Reading the responses on OpenReviews was helpful in providing insight into the difficulties others also had with the paper. The Appendix provided by the authors in response to the OpenReview comments helped clarify questions we had about hyperparameters and implementation.
Discrepancies between the code and the paper, and inconsistencies in the code (misssnng directories, dependencies, hard-coded hyperparamcters, inconsistent nomenclature, improper seeding and GPU allocation, etc.) meant the code required a lot of trial-and-error to produce valid results. For example, the À variable named "_puct'' was reported to be 5 in the paper, but was set to 100 in the config files. This discrepancy was not identified until after the T-GNNExplainer had already been trained. Similarly, the original code was not seeded properly so all experiments had to be ro-trained again. This trial-and-crror, combined with the massive compute requiremenis demanded by the code, meant had to forego many crucial experiments due to lack of computational resources and high associated costs. Using the Machine Learning Emissions Calculator (Lacoste et al. 2019). we estimate that the GPU resources on this project alone emitted at least 103 kg CO2 eq., or equivalent to driving more than 400km by an average ICE car (See Table in Appendix A
What was difficult:
While validating reproducibility essential in machine learning, there a serious conversation to be had about the trade-off between these efforts and the societal and environmental costs incurred when the ground- work to enable reproduction is not 'baked" into the rescarch process from the start.
Communication with original authors: The authors were contacted, but we did not receive response.
Acknowledgements
This research was supported by the Google Cloud Research Credits program with the award 326120695. We'd also like to thank Martin Smit for his close guidance and Florian Golemo for his invaluable advice and support in running the experiments. This work was created in the context the course "Fairness, Accountability, Confidentiality and Transparency in Al" at the Universiteit van Amsterdam (UvA) and we would also like to thank the lecturer, Prof. Fernando Pascal Dos Santos.
References
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, and Marinka Zitnik. Evaluating explainability for graph neural networks, 2023.
Kenza Amara, Rex Ying, Zitao Zhang, Zhihao Han, Yinan Shan, Ulrik Brandes, Sebastian Schemm, and Ce Zhang. Graphframex: Towards systematic evaluation of explainability methods for graph neural networks, 2022.
Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, and Soren Poulsen. Tick: python library for statistical learning, with particular emphasis time-dependent modelling, 2018.
Jenna Burrell. How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data 8 Society, 3(1), 2016. doi:
Chang Chang and Chao Tang. Community dotection for networks with unipartite and bipartite structure. New Journal of Physics, 16(9), 2014. doi: 10.10S8/1367-2000 16/9 99990001.
Gerrit Jan de Bruin, Cor Veenman, H. Jaap van den Herik, and Frank W. Takes. Experimental evaluation of train and test split strategies in link prediction. In Rosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, and Marta Pares (eds.), Complez Networks & Their Applications 1X, pp. 79 91, Cham, 2021. Springer International Publishing.
Finale Doshi-Velez and Been Kim. Towards rigorous science of interpretable machine learning, 2017.
16

---

Farzan Farnia and David Tse. A minimax approach to supervised learning, 2017.
Marie-Josee Fortin, Patrick James, Alistair MacKenzie, Stephanie Melles, and Bronwyn Rayfield. Spatial statisties, spatial regression, and graph theory in ecology. Spotial Statistics, 1:100-109, 05 2012. doi: 22100///.spasta.2112..2..2
Valeria Gelardi, Didier Le Bail, Alain Barrat, and Nicolas Claidiere. From temporal network data to the dy- namics of social relationships. Proceedings of the Royal Society B: Biological Sciences, 288(1959), Septem- ber 2021. ISSN 1471-2954. doi: 10.1009/sspb.2222.116.
Alan Hawkes. Point spectra of some mutually exciting point processes. Journal of the Royal Statistical Sociely. Series B, 33, 07 1971. doi:
Wenchong He, Minh N. Vu, Zhe Jiang, and My T. Thai. An explainer for temporal graph neural networks, 2022.
Srijan Kumar, William Hamilton, Jure Leskovec, and Dan Jurafsky. Community interaction and conflict on the web. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 933 943, International World Wide Web Conferences Stecring Committee, 2018.
Srijan Kumar, Xikun Zhang, and Jure Leskovec. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19. ACM, July 2019. doi:
Alexandre Lacosle, Alexandra Luccioni, Thomas Dandre, and Victor Schmidt. Quantifying the carbon emissions of machine learning, 2019. URL htttps://mco2.. wttp:/ii..bbbb.immmc.eo.eaaeeaee
Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, and Shuiwang Ji. Dig: A turnkey library for diving into graph deep learning research, 2021.
Ana Lucie, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri. Cf-gnnexplainer: Counterfactual explanations for graph neural networks, 2022.
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. Pa- rameterized explainer for graph neural network, 2020.
Fabiola Pereira, João Gama, Sandra Amo, and Gina Oliveira. On analyzing user preference dynamics with temporal social networks. Machine Learning, 107, 11 2018. doi: 10.1007/510994-01-5744433
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. Temporal graph networks for deep learning dynamic graphs, 2020.
Wenwen Xia, Mincai Lai, Caihua Shan, Yao Zhang, Xinnan Dai, Xiang Li, and Dongsheng Li. Explaining temporal graph models through an explorer-navigator framework. In The Eleventh International Confer- ence on Learning Representations, 2023. URL httpp:://openrevie http:/ppperrvvie...eeeffrrrrrrrr
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. Inductive representation learning on temporal graphs, 2020.
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovee. Gnnexplainer: Generating explanations for graph neural networks, 2019.
Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. July 2018. doi: 2119663/jcai.2218////
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. Xgnn: Towards model-level explanations of graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '20. ACM, August 2020. doi: 33.4433333533
17

---

A
Machine Learning Emissions Calculator
In Table 9
we provide the specifications for the calculator used to calculate the CO2 impact.
Table 9: Specifications for the calculator Lacoste et al. 2019
Hardware
Hours used
Provider
Carbon Efficiency
Offset Bought
AMD EPYC 7763
400
Private Infrastructure Google Cloud Platform
0.459
0
T4
2760
0
B
Experimental setup: Novel datasets
Table 10: Hyperparameters used for all models on datasets.
Hyperparameter
TGAT
TGN Synthetic
Real-world
Synthetic
MooC
Real-world
MooC
Hidden Dimension
172
4
172
4
4
Attention heads
2
2
2
2
2
N degree
10
10
10
10
10
10
Memory dimension
172
4
4
Time dimension
172
4
172
4
4
Node feature dimension
172
4
172
4
4
Edge feature dimension
172
4
172
4
4
Training epoch
10
100
100
10
100
100
Learning rate
le
1e-4
le-4
1e
1e
le
Batch size
512
256
512
256
512
256
Dropout probability
0.1
0.1
0.1
0.1
0.1
0.1
The format of the MooC dataset matches the required format of real world datasets. Similar to Reddit and Wikipedia, feature normalization is applied to the dataset. For the Reddit hyperlinks dataset, the concrete preprocessing steps that are taken can be found in the Reddit, Betptt__peelin__ppeeeeeee In summary, min-max normalization is applied to each feature column individually and the datatypes and column names are changed to match the requirements set by the author's code. Once these two steps are completed the instructions in the Read.me file provided can be used to train to T-GNNExplainer on a novel datasct.
file.
The process.py file ensures the non-feature columns exclusively contain two node indexes, a timestamp and label. After processing the file is saved in subdirectory called 'processed", Secondly, indexes ought to be generated for a real-world datasct. This is done by calling 'python tg_ ss__dttatt.pp -d dataset_ Lacasst___ameee____ -c index:: The mead.me file gives the wrong instruction here. This step randomly samples 500 events from the test split on which the performance of the T-GNNExplainer will be evaluated. Before T-GNNExplainer can trained, the GNN's (TGAT and TGN) need to be trained on the new dataset. Make sure that the hyperparameters are correctly specified the run.sh files. For the inductive event prediction performed by the two GNNs, average precision after each epoch is outputted in the terminal. In experimentation, the best achieved average precision is recorded (Table II
Once TGAT and TGN are trained on all datasets. T-GNNExplainer is ready to be trained. The final step the Read.me provides adequate instructions. The Fidelity results are outputted in files called model name lownumber to hio__hhmhmbr..s...
18

---

Table 11: Models' average precision (AP) for the inductive event prediction on all datasets.
Wikipedia
Reddit
Synthetic
Synthetic v2
MooC
New 0.971
0.982
0.945
0.953
0.626
TGAT
Old
0.979
0.975
0.963
0.964
0.706
New 0.947
0.980
0.945
0.960
0.659
TGN
Old
0.985
0.966
0.954
0.969
0.712
19


Reviews:
[{"review_id": "Qu1LXx8g4z", "reviewer": "Reviewer_JK87", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "---\n\nStrengths:\n\n+++ authors appear to have conducted a thorough reproduction, in partilar disambiguating hyperparameters and overcoming other common obstactles to reproducibility\n \n++ upon finding divergence, further investigation was performed in order to attempt to explain divergence, including adding a new dataset\n \n\\+ presentation is at times idosyncratic (see below) but clear and understandable. \n\n\nWeaknesses:\n(---, maybe?) unsure whether this counts as (self?)-plagiarism, but the appendix in the supplementary appears to be 1:1 the supplementary of the underlying paper\n \n--- while I understand and empathize with the computational constraints, no mention is made of multiple seeds (i.e., trianing multiple models) nor is there a systematic evaluation of possible randomness in the results - since the original paper does not mention training multiple seeds per model either, this is understandable, but it leaves occams razor for the failed reproduction (variability across seeds) unexplored.\n \n--- likewise, in order to be meaningful, some of the observations really need statistical significance test, in particular the regression fit in fig 2a and the results reported in tables (the latter also needs evaluation of variability).\n \nwhile I want to state that these are weaknesses inherited from the original paper, they remain hindrances for me to full recomennd this paper, especially without explicit acknowledgement.\nI think the compute budget should have gone to those additional seeds and significance tests first, not on attempting to add more datasets.\n \n-- why was the percentage difference computed in a way to make \"it not possible to compute percentage differences\" of metrics on page 10/11? what hinders us from normalizing the values to 0-1 and then computing scores, or just from properly defining the percentage calculation to ensure sensible results? \n \n\\- at some points the writing can be a bit unclear. E.g., in 8.2, \"did not score a high accuracy on MOOC compared to the other datasets\" is followed by (96% compared to 66% respectively, I presume 66% refers to mooc and 96% to the others, but the flip is confusing)\n \n(-) nitpick: some typos/leftover from wanting to add two datasets: page 11 bottom, page 12 top and some other places refer to \"datasets\" where it should be singular, page 12 top also says \"it would fruitful\" where it should read \"it would *be* fruitful\". Also, broken reference to fig 2b in 8.4\n \n\n(-)  earlier in the paper the authors refer to \"connections in both directions\"; in 8.5. paragraph 2, you correctly refer to \"both sets\"; is this another small mistake?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors performed a reproducibility study of \"Explaining Temporal Graph Models Through an Explorer-Navigator Framework\" focusing on a subset of the main claims, specifically the claim of TGNNE outperforming baselines by up to 50% and agnosticism w.r.t to the underlying predictor.\n\nFor this they reproduced the original paper using the papers supplementary, disambiguating paper/code divergences in favour of the paper.\nThey find varying behaviour on multiple levels, first receiving markedly different final accuracies and evaluation metrics on the original datasets, second finding that the TGNNE does not outperform all baselines on all datasets, third finding that there is a dependency on the underlying predictor models and datasets.\nThe authors also extend experimentation by attempting to train and evaluate on the MOOC dataset and perform an analysis of the dependence on dataset structures, claiming a dependence on subgraph density.\n\nFinally, challenges encountered in the reproducibility study are remarked upon, in particular the difficulty of reproducing papers not released with reproducibility \"built in\"", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Critical: Please perform suitable statistical signficance tests, adding training seeds and evaluations until the results are meaningful *OR* strongly disclose the limitations implied by not doing so. I acknowledge the difficulty of compute constraints, but would request *at least* adding one or two additional runs to establish a certain minimum of variability investigation + a disclosure that more is required if compute is the limitation (this can then further be discussed in the paper).\n\nDoing at least one of these is critical, otherwise I cannot claim that this paper is supported in its claims by evidence, much as I appreciate reprodicibility work.\n\nStrengthening:\nAside from this, I would adjust the reporting of differences and clean up the writing/typo nitpicks I voiced.", "broader_impact_concerns": "I think the broader impact concerns are appropriate", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "NFxV92hA8W", "reviewer": "Reviewer_H3zU", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\nGraph explainers are a timely topic with many important applications from biology and chemistry to e-commerce and weather forecasting.\n\nThe authors perform a rigorous analysis of the referred paper, examining each claim separately and go to notable extend to make their work easily reproducible and accessible. \n\nA new dataset is tested and the new results suggest a drop of the performance, although the overall ranking is retained.\n\nA pattern is uncovered regarding the relationship between the number of nodes of the “explainer” subgraph and the performance i.e. the more dense the better the metric.\n\nWeaknesses:\n\nThe experiments are limited, in the sense that error bars are missing.\n\nThe study needs to be more broad to constitute a contribution. \n\nThe authors could elaborate more on the uncovered patterns, such as the one regarding the density of the subgraph and the explainability e.g. ablation studies to further butress the observation and potentialy test the potential of the pattern to improve the results.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper replicates the findings of Xia et al. (2023) for the temporal GNN explainer. TGNNExplainer is a navigator neural network that learns to detect subgraphs that are sufficient to classify correctly a particular event in the dataset i.e. explain sufficiently the predicted event. The authors verify the original tests, ensure their clear reproducibility and compare with the same baseline methods, in order to affirm T-GNNExplainer's superior performance in the predefined metrics. The study also reveals some degree of variability in performance across different models and datasets and some incosistencies with the original paper results. This uncovers the need for more elaborate experiments and consideration of alternative methods.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "The experiments need to be validated across many different seeds to quantify the robustness of the method.\n\nTo increase the scope of the study sufficiently, the authors could include more real world datasets varying in size and time steps. Moreover, as the author suggest in future work, it is meaningful to examine the effectiveness in node and graph tasks, and not only on bipartites tasks. The methods are also limited as more GNN architectures like GCN, SAGE and GIN should be tested to increase the scope of the analysis.\n\nFinally, the paper needs proofreading e.g. There is a table captions called “Percentage % difference between best and best baseline explainer” also, some typos e.g. Figure ??, Figure 2 ??", "broader_impact_concerns": "There is no obvious ethical concern regarding the imapct of the study.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "RQULOrlDm4", "reviewer": "Reviewer_qRyC", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "## Strengths\n1. Reproducibility is important in machine learning, and this paper, (I don't know whether it should be 'fortunately' or 'unfortunately') identifies a paper that is not 'so' reproducible. \n2. The authors identify the necessary steps needed to run the code of the paper, which is even missing from the original authors. I appreciate the efforts it takes to fill the missing steps. \n3. The MOOC dataset is of different properties as Reddit and Wikipedia (the original datasets), and indeed, results on MOOC is quite different from those obtained with wikipedia. \n\n## Weaknesses\n1. I would like to first question the impact of whether the reproducibility report would make an impact. As of March 2024 when I review this paper, the studied \"T-GNNExplainer\" only has 5 citations in about one year. This means that the T-GNNExplainer itself has rather limited impact, and I am not sure whether reproducing this would raise the interests of related researchers. Of course there will be 'some' researchers who are interested, but I am not sure whether it is sufficient to motivate the paper. \n2. The paper's organization has to be improved, especially within the section of experimental results. For example, the authors could have shown complete Tables 2 and 3 with both datasets and both models, but they did not, which make reading the experimental results quite difficult. \n3. I think that the hyperparameter tuning is necessary as an ML model should be tested on multiple hyperparameters to determine its performance, but the authors did not do so. \n4. Miscellaneous: \n     - There are broken references '??'\n     - Section 3.2.1 can be better written by giving some intuitions on the Equations. \n     - Figure 1 can be better plotted with clearer legends to separate different models. \n     - It seems that PBONE performs very differently between 'old' and 'new'. Any reasons why this may happen?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents a reproducibility report of the paper \"Explaining Temporal Graph Models Through an Explorer-Navigator Framework\". The authors re-run the experiments for baselines and also TGNN-Explainer, and also propose a new dataset MOOC for evaluation. The results obtained with the reproducibility report is that TGNN-Explainer does not outperform baselines as much as it claims to do.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "See the \"Weaknesses\".", "broader_impact_concerns": "No.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The manuscript delivers a reproducibility study pertaining to the paper “Explaining Temporal Graph Models Through an Explorer-Navigator Framework” (Xia et al., 2023).  Reviewers generally appreciated the efforts made for conducting the study, as well as the solidity of its findings.  There were some concerns with regards to statistical significance and clarity of writing, but the authors largely addressed those.  A point of disagreement within the committee relates to the limited significance and novelty of the work, and in particular to whether these limitations should be an impediment for publication.  While I share the sentiment that they may constrain the manuscript's future impact, TMLR's acceptance criteria deliberately avoids rejection on the grounds of such sentiments (especially for reproducibility studies).  I am therefore recommending acceptance, leaving it to TMLR readership to determine the impact of the contribution.
Decision: Accept (poster)