Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Authors: Zhaocheng Zhu, Xinyu Yuan, Michael Galkin, Louis-Pascal Xhonneux, Ming Zhang, Maxime Gazeau, Jian Tang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration. On a million-scale dataset ogbl-wikikg2, A*Net not only achieves a new state-of-the-art result, but also converges faster than embedding methods. |
| Researcher Affiliation | Collaboration | 1Mila Québec AI Institute, 2University of Montréal 3Intel AI Lab, 4Peking University, 5LG Electronics AI Lab 6HEC Montréal, 7CIFAR AI Chair |
| Pseudocode | Yes | Algorithm 1 A*Net |
| Open Source Code | Yes | Code is available at https://github.com/DeepGraphLearning/AStarNet |
| Open Datasets | Yes | We evaluate A*Net on 4 standard knowledge graphs, FB15k-237 [40], WN18RR [16], YAGO3-10 [30] and ogbl-wikikg2 [25]. |
| Dataset Splits | Yes | For the transductive setting, we use the standard splits from their original works [40, 16]. For the inductive setting, we use the splits provided by [39], which contains 4 different versions for each dataset. |
| Hardware Specification | Yes | We train A*Net with 4 Tesla A100 GPUs (40 GB) |
| Software Dependencies | No | The paper mentions developing based on an open-source codebase but does not specify version numbers for any software dependencies like Python, PyTorch, or DGL. |
| Experiment Setup | Yes | For the neural priority function, we have two hyperparameters: K for the maximum number of nodes and L for the maximum number of edges. To make hyperparameter tuning easier, we define maximum node ratio α = K/|V| and maximum average degree ratio β = L|V|/K|E|, and tune the ratios for each dataset. The maximum edge ratio is determined by αβ. The other hyperparameters are kept the same as the values in [58]. |