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..
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks
Authors: Jianxin Ma, Peng Cui, Wenwu Zhu
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world networks. Empirical results demonstrate that our approach can achieve significant performance gain over existing approaches. |
| Researcher Affiliation | Academia | Jianxin Ma, Peng Cui, Wenwu Zhu Department of Computer Science and Technology, Tsinghua University, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 The Prediction Routine; Algorithm 2 The Training Routine |
| Open Source Code | No | No explicit statement or link for open-source code is provided for the methodology described in this paper. |
| Open Datasets | Yes | DBLP: We extract a co-author network from dblp.org... PPI (Breitkreutz et al. 2008)... Blog Catalog (Tang and Liu 2009) |
| Dataset Splits | No | The paper describes a training procedure that samples subgraphs and treats some nodes as out-of-sample for empirical risk minimization, but it does not specify explicit train/validation/test dataset splits for the overall datasets (DBLP, PPI, Blog Catalog) for hyperparameter tuning or model selection. |
| Hardware Specification | No | No specific hardware models (e.g., GPU/CPU types) are mentioned for the experimental setup. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use g(x) = x + g(x) for Depth LGP, where g(x) is a neural network with a single hidden layer of 64 units. We use Leaky Re LU as the activation function. ... We set the number of seed nodes to be four when sampling a subgraph for training. |