Multi-hop Attention Graph Neural Networks
Authors: Guangtao Wang, Rex Ying, Jing Huang, Jure Leskovec
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on node classification as well as the knowledge graph completion benchmarks show that MAGNA achieves state-ofthe-art results: MAGNA achieves up to 5.7% relative error reduction over the previous state-of-the-art on Cora, Citeseer, and Pubmed. MAGNA also obtains the best performance on a large-scale Open Graph Benchmark dataset. On knowledge graph completion MAGNA advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics. |
| Researcher Affiliation | Collaboration | 1JD AI Research 2Computer Science, Stanford University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'code will be released after publication.' This does not provide concrete access at the time of publication. |
| Open Datasets | Yes | We employ four benchmark datasets for node classification: (1) standard citation network benchmarks Cora, Citeseer and Pubmed [Sen et al., 2008; Kipf and Welling, 2016]; and (2) a benchmark dataset ogbn-arxiv on 170k nodes and 1.2m edges from the Open Graph Benchmark [Weihua Hu, 2020]. |
| Dataset Splits | Yes | We follow the standard data splits for all datasets. Further information about these datasets is summarized in the Appendix. [...] We use the standard split for the benchmarks, and the standard testing procedure of predicting tail (head) entity given the head (tail) entity and relation type. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For datasets Cora, Citeseer and Pubmed, we use 6 MAGNA blocks with hidden dimension 512 and 8 attention heads. For the large-scale ogbn-arxiv dataset, we use 2 MAGNA blocks with hidden dimension 128 and 8 attention heads. |