Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
Authors: Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we provide numerical experiments to demonstrate the validity of our analysis and the effectiveness of the proposed learning algorithm for GNNs. [...] Section 5 shows the numerical results |
| Researcher Affiliation | Collaboration | 1Dept. of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, NY, USA 2MIT-IBM Waston AI Lab, Cambridge, MA, USA 3IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. |
| Pseudocode | Yes | Algorithm 1 Accelerated Gradient Descent Algorithm with Tensor Initialization |
| Open Source Code | No | The paper does not provide any links or explicit statements about releasing open-source code for the described methodology. |
| Open Datasets | No | We verify our results on synthetic graph-structured data. [...] The feature vectors {xn}N n=1 are randomly generated from the standard Gaussian distribution N(0, Id d). |
| Dataset Splits | No | The paper mentions "Partition Ωinto T = log(1/ε) disjoint subsets, denoted as {Ωt}T t=1;" for the algorithm, but does not specify a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Input: X, yn n Ω, A, the step size η, the momentum constant β, and the error tolerance ε; [...] The dimension d of the feature vectors is chosen as 10, and the sample size |Ω| is chosen as 2000. [...] The initialization is randomly selected from W (0) W (0) W F / W F < 0.5 to reduce the computation. |