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 | Conference PDF | Archive PDF | Plain Text | 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. |