Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: Authors propose a novel aggregation scheme, i.e., adaptive distance message passing aggregation, to addresses thr influence induced by the different types of edges in the graphs. Experiments on real-world datasets validate the effectiveness of our method.
strengths_and_weaknesses: [Strength]:
- The motivation is clear, i.e., addressing the the negative impact brought by different types of edges in a graph.

[Weakness]:
- The solution is not well stated, e.g., how to select the value of k+n-hop to perform the adaptive distance message passing? Is n a learnable parameter? I did not find where it state how to learn this paremeter. 
- The experimental results seem not to be very significant, which just improve 1/1000 on zinc.

suggested_changes: - The solution need to be polished and the comparison experiment need to be redesigned.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper introduces an adaptive distance message passing scheme for graph neural networks. It aims to capture the types of edges in the graph by adapting how far the messages from different types of edges travel. The idea is applied on two different datasets and results are presented.
strengths_and_weaknesses: Strengths:
The paper states the proposed idea clearly and effectively communicates it. The motivation for the adaptive distance message passing scheme is well presented.

Weaknesses:
It is not clear whether the proposed methodology depends on the dataset in question. Since the number of edge-types is a factor, how do we use ADMPE if edge-type information is unknown or unclear? How much domain knowledge is necessary?
suggested_changes: 1. Does the methodology have to be updated depending on the learning task? Specifically, do we have to know how many types of edges are there in the dataset ahead of time? 
2. How many times were the experiments run? Is only the mean value presented in Table 1?
review_title: null

Decision: Invite to archive