Federated Graph Classification over Non-IID Graphs
Authors: Han Xie, Jing Ma, Li Xiong, Carl Yang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed frameworks. |
| Researcher Affiliation | Academia | Han Xie, Jing Ma, Li Xiong, Carl Yang Department of Computer Science, Emory University {han.xie, jing.ma, lxiong, j.carlyang}@emory.edu |
| Pseudocode | No | The paper describes algorithms and their steps but does not contain a formal pseudocode or algorithm block. |
| Open Source Code | Yes | We include our models and code in the supplemental material |
| Open Datasets | Yes | We use a total of 13 graph classification datasets [30] from three domains including seven molecule datasets (MUTAG, BZR, COX2, DHFR, PTC_MR, AIDS, NCI1), three protein datasets (ENZYMES, DD, PROTEINS), and three social network datasets (COLLAB, IMDB-BINARY, IMDB-MULTI), each with a set of graphs. Node features are available in some datasets, and graph labels are either binary or multi-class. Details of the datasets are presented in Appendix C. [30] Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. Tudataset: A collection of benchmark datasets for learning with graphs. In ICMLW, 2020. |
| Dataset Splits | Yes | The two important hyper-parameters ε1 and ε2 as clustering criteria vary in different groups of data, which are set through offline training for about 50 rounds following [33]... can be easily set through some simple experiments on the validation sets following [33]. |
| Hardware Specification | Yes | We run all experiments for five random repetitions on a server with 8 × 24GB NVIDIA TITAN RTX GPUs. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not specify software dependencies with version numbers (e.g., Python, specific libraries like PyTorch or TensorFlow). |
| Experiment Setup | Yes | We use the three-layer GINs with hidden size of 64. We use a batch size of 128, and an Adam [20] optimizer with learning rate 0.001 and weight decay 5e-4. The μ for Fed Prox is set to 0.01. For all FL methods, the local epoch E is set to 1. |