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..
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Authors: Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical findings are also justified through numerical experiments. ... We will focus on numerical evaluations on synthetic data where we can control target functions and compare with A explicitly. |
| Researcher Affiliation | Collaboration | 1Department of Electrical, Computer, and System Engineering, Rensselaer Polytechnic Institute, NY, USA 2Department of Computer Science and Engineering, Michigan State University, MI, USA 3MIT-IBM Watson AI Lab, IBM Research, MA, USA 4IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA 5Department of Computer Science and Engineering, University at Buffalo, NY, USA. |
| Pseudocode | Yes | Algorithm 1 Training with SGD and graph topology sampling |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | We generate a graph G with N = 2000 nodes. ... Synthetic labels are generated based on (20) using A as A. ... The paper uses synthetic data generated for the experiments and does not provide access information (link, DOI, or citation) to this generated dataset. |
| Dataset Splits | No | We generate a graph G with N = 2000 nodes. ... A three-layer GCN as defined in (4) with m neurons in each hidden layer is trained on a randomly selected set Ωof labeled nodes. The rest N |Ω| labels are used for testing. ... The paper describes a split into training (|Ω|) and testing (N - |Ω|) sets, but does not explicitly mention or detail a separate validation split. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper describes the algorithms and theoretical framework but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with specific versions) used for implementation. |
| Experiment Setup | Yes | The learning rate η = 10 3. The mini-batch size is 5, and the dropout rate as 0.4. The total number of iterations is TTw = 4|Ω|. Our graph topology sampling method samples S1 = 0.9N1 and S2 = 0.9N2 nodes for both groups in each iteration. |