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..
Robust Graph Representation Learning via Neural Sparsification
Authors: Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both benchmark and private datasets show that Neural Sparse can yield up to 7.2% improvement in testing accuracy when working with existing graph neural networks on node classification tasks. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of California, Los Angeles, CA, USA 2NEC Laboratories America, Princeton, NJ, USA. |
| Pseudocode | Yes | Algorithm 1 Training algorithm for Neural Sparse |
| Open Source Code | No | The paper mentions 'The supplementary material contains more experimental details.' but does not explicitly state that source code for the methodology is provided or offer a link to a code repository. |
| Open Datasets | Yes | We employ five datasets from various domains and conduct the node classification task following the settings as described in Hamilton et al. (2017) and Kipf & Welling (2017). The dataset statistics are summarized in Table 1. |
| Dataset Splits | Yes | The dataset statistics are summarized in Table 1. ... Training Nodes, Validation Nodes, Testing Nodes are listed for each dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'tensorflow' as a deep learning framework but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) used in their experimental setup. |
| Experiment Setup | Yes | Temperature tuning. We anneal the temperature with the schedule τ = max(0.05, exp( rp)), where p is the training epoch and r 10{ 5, 4, 3, 2, 1}. τ is updated every N steps and N {50, 100, ..., 500}. ... For Reddit, PPI, Transaction, Cora, and Citeseer, the hyperparameter k is set as 30, 15, 10, 5, and 3 respectively. The hyper-parameter l is set as 1. |