DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks
Authors: Jianxin Ma, Peng Cui, Wenwu Zhu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 majx13fromthu@gmail.com, cuip@tsinghua.edu.cn, wwzhu@tsinghua.edu.cn |
| 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. |