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..
Cross-Graph Learning of Multi-Relational Associations
Authors: Hanxiao Liu, Yiming Yang
ICML 2016 | Venue PDF | 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 EMAIL Yiming Yang EMAIL 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). |