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..
Multi-Vector Embedding on Networks with Taxonomies
Authors: Yue Fan, Xiuli Ma
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments reveal HIME s comprehensive advantages over existing methods on tasks such as proximity search, link prediction and hierarchical classification. |
| Researcher Affiliation | Academia | Key Laboratory of Machine Perception (MOE), School of Artificial Intelligence, Peking University, Beijing, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 The LRU policy. |
| Open Source Code | Yes | Code and Appendix: https://github.com/Yue Fan1014/HIME. |
| Open Datasets | Yes | A human PPI network [Szklarczyk et al., 2021] with the Cellular Components domain of Gene Ontology [Ashburner et al., 2000] being the taxonomy. A human gene regulatory network [Liu et al., 2015] depicts regulations among genes, with each gene associated with several biological pathways given by CTD [Davis et al., 2020]. All the pathways are organized as a pathway ontology [Petri et al., 2014]. We extract a dense subset of the DBLP coauthorship network with ACM key word taxonomy. |
| Dataset Splits | No | We split the node-node links with the ratio of 7:3 and the node-label links with the ratio of 9:1 for training and testing. No explicit mention of a validation set or split was found. |
| Hardware Specification | No | No specific hardware details (e.g., GPU model, CPU type, memory) used for running the experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) were provided. |
| Experiment Setup | Yes | In the experiments, we set the embedding dimensions of all methods to 256. The branch vector dimensions are 128, 64, 32 for HIME 2, HIME 4 and HIME 8 respectively so as to ensure that a node s total dimension is no greater than 256. All methods are tuned to the best and trained for 100 epochs. |