Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper provides a method for link prediction based on random walk based pooling method (WalkPool). It aggregates the probabilities of the adjacent structure of a node or link to design the node embeddings. Finally, it shows superiority of the method against baselines via AUC and AP.
main_review: Strengths:
(1) The paper is clearly written
(2) I find the walk based approach to be interesting (with several weaknesses)

Weakness:
No conceptual difference with GNN: In my understanding, when we compute $P^{\tau}$, it combines the $\tau$ hop information around a node to compute its embedding. In this aspect, I did not understand any conceptual difference with GNN. What aspect of it is making the difference?

Loss function: At the end, LP is a classification task. From this aspect, MSE loss minimization may not be suitable--- a simple classification or ranking loss should be more suitable. Did the authors try them?

AUC: I suspect that for most of the datasets, the difference of WALKPOOL with baselines is not statistically significant. Can the authors clarify about it?


summary_of_the_review: The walk based approach is interesting but the paper lacks novelty, clear articulation about high or low level difference of WalkPool with GNN. Also, MSE  loss is chosen for a clear classification task. The results are weak too.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a neural architecture for predicting missing links in a graph by designing a so-called WalkPool scheme. WalkPool exploits node features (from a GNN) to compute the transition probability between the nodes, and then manually design a number of links that are extracted from the oder-k encolosing sub-graphs covering a pair of nodes for link prediction. The authors also defined a delta-graph feature that is the sub-graph level feature difference before and after removing the node pairs with which the link is to be predicted. Experimental results are quite optimistic on about 10 benchmark datasets.
main_review: Strengths: 

The paper is well-written; the introduction of the previous work is organized. Description of the SEAL algorithm is very insightful and clear.

Weaknesses:

The attributed network datasets concsidered is a bit limited. Can there be more datasets available for evaluation?

The final features extracted are mainly specified by human. Though effective, it leaves an open question that whether thses sorts of features can be leared adaptively?

The proposed method follows the framework of the SEAL algorithm, and the authors should exphasize the differences between the two methods and what makes the propose method more advantageous. 
summary_of_the_review: 
Overall the paper is nicely written and organized, and provides useful thought in the problem of link prediction with or without node features. Experimental results are also promising. However I have a few technical details I would like to discuss.

(1) The authors used feature-based similarty (like in GAT type of algorithms) to define the transition probbaility matrix, which takes the place of the topology-based transitiona matrix by and large. Is there a way to combine the information from both?

(2) The pooling part is a bit confusing to me, in that it seems that the WALKPOOL indeed does not perform pooling but instead use pre-defined features, while pooling is usually for a set (of objects such as nodes) with varying numbe of elements. Can authors add more details of the pooling used in the SEAL and why WALKPOOL is considered a pooling scheme?
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper proposes a new link prediction method called WP, based on treating link prediction as a subgraph classification problem, where the link of interest is surrounded by the subgraph. Existing GNN architectures could be used for learning node embeddings. With node embeddings, a latent subgraph could be built using self-attention, where attention scores are used for computing the subgraph edge weights. With the latent subgraph, a set of features could be computed to be used as the features for the link of interest. The features will be used for subsequent link prediction. Essentially, the proposed WP method is a feature extractor for links (or its surrounding subgraph).
main_review: Strengths:
1. Building the latent subgraph from embeddings is an interesting idea. 
2. The extract features somehow encodes path information which may be useful for link prediction.
3. Comprehensive experiments are conducted.

Weaknesses:
1. Motivations in the Introduction section are not clearly stated.
summary_of_the_review: The paper proposes a pooling method (i.e., a latent structure feature extractor) for learning features that describe links (i.e., the surrounding subgraphs of the links). Building the latent subgraph from embeddings turns continuous information back to discrete graph space, which facilitates feature extraction. The framework remains end-to-end learnable. The extracted features summarizes multi-hop path information, which somehow reflect higher order structural information. Experiments on multiple real datasets are conducted.

In general, the paper proposes a neat idea with good experiment design. The current draft could be further improved by revising its Introduction section, as many claims are not clear and do not seem to motivate the method design. For example, Figure 1 does not provide much information about the idea. Also, I think the word "topology" does not well describe the proposed idea.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper introduces WalkPool, a model for link prediction that relies on GNNs. The main idea is to jointly encode node representations and graph topology information into node features which could be learned in an end-to-end-manner. The proposed problem formulation is interesting: the transition probabilities to capture proximity among nodes are computed on top of a latent graph which is the outcome of an attention mechanism on node features. The proposed methodology is presented clearly. Besides, the authors have performed experiments on various graphs, and the performance of WalkPool is compared against state-of-the-art baseline models.
main_review: Strengths:
- The paper provides an elegant formulation of the link prediction problem. The execution of the proposed methodology is also interesting.
- The paper is clearly written, presenting the proposed model in a rigorous way but at the same time providing good motivation.
- The experimental evaluation is quite thorough. 

Weaknesses:
-	Lack experiments on benchmark (artificial) graphs. This could further increase our understanding of the functioning of the model
-	The baseline models could be enhanced. Although the list is quite complete, some of the used models are not tailored for the link prediction task.

**Detailed comments.**

As I mentioned above, I really enjoyed reading the paper. Despite building upon ideas presented in  SEAL, the formulation is interesting. Besides, the arguments made seem to be valid. Yet, some points are not very clear and need further elaboration. Next, I summarize key points related to the paper.

To begin with, although the paper uses standard benchmarks for link prediction, I would expect to have some experiments on artificial datasets in order to better understand the functioning of the model. As I can see, most of the networks used have really high clustering coefficient---which is not the case for the majority of real-world graphs. Having artificial networks where one could control ACC would be very helpful.

In the case of the absence of node attributes, the paper concatenates the outputs of the three-layer to obtain the final node representation. Aren’t the embeddings of the final layer enough for this? Please elaborate more on this aspect.

Although spectral clustering has been widely used before, I think it is not an appropriate baseline for link prediction. I would rather be in favor of using other unsupervised feature learning models (e.g., LINE, NetMF).

The datasets used are quite small in terms of the number of nodes and edges, and there is no discussion about the running time of the proposed model. How does WalkPool’s running time compare against SEAL?


summary_of_the_review: Overall, I believe it’s an interesting approach that offers a new perspective on the link prediction task. Despite building upon ideas presented in previous work (SEAL), the paper makes progress in presenting an elegant methodology. Some aspects related to the experimental evaluation should be addressed.
review_title: null

Meta-review: This paper proposes a new link prediction algorithm based on a pooling scheme called WalkPool. The main idea is to jointly encode node representations and graph topology information into node features and conduct the learning end-to-end. The paper shows the superiority of the method against the baselines.

Strength
* The paper is generally clearly written.
* A new method is proposed, which is technically sound.
* Many experiments are conducted to verify the effectiveness of the proposed method.

Weakness
* The novelty of the work might not be so significant.  There is a similarity with the SEAL algorithm.

The authors have addressed most of the problems pointed out by the reviewers. They have also conducted additional experiments.
Decision: Accept (Poster)