Few-shot Relational Reasoning via Connection Subgraph Pretraining
Authors: Qian Huang, Hongyu Ren, Jure Leskovec
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real KGs, including NELL, FB15K-237, and Concept Net, demonstrate the effectiveness of our framework: we show that even a learning-free implementation of CSR can already perform competitively to existing methods on target few-shot tasks; with pretraining, CSR can achieve significant gains of up to 52% on the more challenging inductive few-shot tasks where the entities are also unseen during (pre)training. |
| Researcher Affiliation | Academia | Qian Huang Stanford University qhwang@cs.stanford.edu Hongyu Ren Stanford University hyren@cs.stanford.edu Jure Leskovec Stanford University jure@cs.stanford.edu |
| Pseudocode | Yes | Algorithm 1 Hypothesis Proposal Module Mp of CSR-GNN Algorithm 2 CSR-GNN Full Architecture |
| Open Source Code | Yes | The implementation of CSR can be found in https://github.com/snap-stanford/csr. |
| Open Datasets | Yes | We evaluate our method CSR-OPT and CSR-GNN on few-shot KG completion tasks over three real world KGs NELL, FB15K-237 and Concept Net. We take NELL directly from NELL-One [23]... FB15K-237 and Concept Net following [12, 23]. |
| Dataset Splits | Yes | We summarize the statistics of all three datasets in Table 1. BG refers to the background KG available during training time. Trans-BG, Ind-BG, Ind-Test. |
| Hardware Specification | No | The paper states: "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix." However, no specific hardware details (like GPU/CPU models, memory, or specific cloud instance types) are provided in the main body of the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) in its main text. |
| Experiment Setup | Yes | For simplicity, we only consider the number of few shot example K = 3, even though all methods here can generalize to arbitrary K. See appendix A for more details on full experiment setups. |