Cross-Graph Learning of Multi-Relational Associations
Authors: Hanxiao Liu, Yiming Yang
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments with a subset of DBLP publication records and an Enzyme multi-source dataset, the proposed method successfully scaled to the large cross-graph inference problem, and outperformed other representative approaches significantly. |
| Researcher Affiliation | Academia | Hanxiao Liu HANXIAOL@CS.CMU.EDU Yiming Yang YIMING@CS.CMU.EDU Carnegie Mellon University, Pittsburgh, PA 15213, USA |
| Pseudocode | Yes | Algorithm 1: Transductive Learning over Product Graph (TOP) |
| Open Source Code | No | The paper does not provide any statement about making its source code available, nor does it include a link to a code repository. |
| Open Datasets | Yes | We evaluate our method on real-world data in two different domains: the Enzyme dataset (Yamanishi et al., 2008) for compound-protein interaction and the DBLP dataset of scientific publication records. |
| Dataset Splits | Yes | For both datasets, we randomly sample one third of known interactions for training (denoted by O), one third for validation and use the remaining ones for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory, or specific computing environments). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | Following (Duchi et al., 2011), we allow adaptive step sizes for each element in α. That is, in the t-th iteration we use η(t) k1,...,k J = η0 .h Pt τ=1 α (τ) k1,...,k J 2 as the step size for αk1,...,k J, where α (τ) k1,...,k J t τ=0 are historical gradients associated with αk1,...,k J and η0 is the initial step size (set to be 1). |