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 | Conference PDF | Archive PDF | Plain Text | 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]. |