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..
Federated Graph Classification over Non-IID Graphs
Authors: Han Xie, Jing Ma, Li Xiong, Carl Yang
NeurIPS 2021 | Venue PDF | 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 EMAIL |
| 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. |