reviews: 
Comments: This paper focuses on path-based GNNs for knowledge graph reasoning and proposes two modifications to existing models: (1) sampling of important paths (edges) in the graph to reduce the number of visited edges; (2) further sub-sampling of edges based on pre-computed counting features of overlapping relations.
Strengths: N/A
Weaknesses: This path sampling section of DDLR is a blatant plagiarism of the work “A\*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs” [1] available on arxiv since June 2022, I’ll therefore refer to the original 2022 work as “the A\*Net paper” or “A\*Net”, and to this ICLR submission as “DDLR”.

This submission (DDLR) copies almost verbatim several sections of the A\*Net paper including images and equations without any reference or citation of the original work:
* Section 1: Figure 1 in DDLR is a re-colored version of Figure 2(a) in A*Net. 
* Section 3 (Preliminary): The Knowledge Graph Completion paragraph is a copy of the same paragraph in the A*Net with some words replaced by synonyms, eg, 
    * [A*Net] “Each fact is a triplet $(x,r,y) \in \mathcal{V} \times \mathcal{R} \times \mathcal{V}$, which indicates a relation $r$ from entity $x$ to entity $y$.”
    * [DDLR] “Each fact is a triplet $(x,r,y) \in \mathcal{V} \times \mathcal{R} \times \mathcal{V}$, signifying the presence of relation $r$ from entity $x$ to entity $y$.”
* Section 3 (Preliminary): The Knowledge Graph Completion paragraph
    * [A*Net] The task of knowledge graph reasoning aims at answering queries like $(u,q,?)$ or $(?,q,u)$. … we assume the query is $(u,q,?)$, since $(?,q,u)$ equals to $(u,q^{-1},?)$ with $q^{-1}$ being the inverse of $q$.
    * [DDLR] Knowledge graph completion is a task focused on predicting answers for queries such as $(u,q,?)$ or equivalently $(u,q^{-1},?)$, where $q^{-1}$ represents the inverse relation of $q$. **Here the authors make a mistake because $(u,q^{-1},?)$ is not equivalent to $(u,q,?)$ but instead is equivalent to the head prediction task to $(?,q,u)$.**
* Section 3 (Preliminary) -> Path-based Approaches: almost verbatim copy of the text, equation 1, and the same notation from the A\*Net paragraph Path-based Methods.
* Section 4.1 (Path Sampling) -> Important Paths for Reasoning: the whole paragraph is a verbatim copy of Section 3.1 (Important Paths for Reasoning) from the A*Net paper with non-sensical word replacements, eg
    * [A*Net] Given a query relation and a pair of entities, only some of the paths between the entities are important for answering the query.
    * [DDLR] Given a query and **a lot of** entities, only some of the paths between the entities are important for answering the query.
* Section 4.1 (Path Sampling) -> Path Scoring includes copy-paste excerpts from A\*Net’s paragraphs Iterative Computation of Important Paths, Reasoning with A* Algorithm, and Neural Priority function. Particularly,
    * Equations 3,4,5,7 (DDLR) are the same as Equations 6,8,9,10 from A*Net with the same notation and descriptions
    * The priority function $s_{uq}(x)$ is named as “learnable path scoring function” in DDLR instead of “neural priority function” in A\*Net

Even if we omit the borrowed A\*Net content from the list of contributions, the paper boils down to pre-computing co-occurences of all pairs of relations and tuning the threshold manually for each dataset - this is not a substantial algorithmic contribution per se.

Overall, I believe the paper should be rejected as it clearly violates the ICLR Code of Ethics (that authors are supposed to accept upon the submission) and escalated to the AC / SAC / PC level for further investigation.


[1] https://arxiv.org/abs/2206.04798
Recommendation: 1: strong reject
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 1 poor
Clarity: 1 poor