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..
Serving Graph Compression for Graph Neural Networks
Authors: Si Si, Felix Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on semi-supervised node classification demonstrate that the proposed method can significantly reduce the serving space requirement for GNN inference. (Abstract) 4 EXPERIMENTAL RESULTS (Section title) |
| Researcher Affiliation | Collaboration | 1Google Research 2University of California, Los Angeles EMAIL {chohsieh}@ucla.cs.edu |
| Pseudocode | Yes | Algorithm 1 The Virtual Node Graph (VNG) algorithm |
| Open Source Code | No | The paper mentions using a third-party open-source implementation for GCN model training ("Cluster GCN s tensorflow implementation"), but does not state that the code for their proposed VNG method is open-source or provided. |
| Open Datasets | Yes | All above datasets are publicly available and are commonly used for benchmarking the performance of GNNs on node classification tasks. (Section 4) Arxiv: ... We use the same dataset and partition as in (Hu et al., 2020). (Section 4) Reddit: ... We use the same dataset and partition as in (Chiang et al., 2019). (Section 4) Product: ... based on a different preprocessing and split by Hu et al. (2020). (Section 4) Amazon2M: ... We use the same dataset and partition as in (Chiang et al., 2019). (Section 4) |
| Dataset Splits | Yes | Table 2: The statistics of Arxiv, Reddit, Product, and Amazon2M datasets. #Training Nodes #Validate Nodes #Labels #Features Serving size Arxiv: ... We use the same dataset and partition as in (Hu et al., 2020). Reddit: ... We use the same dataset and partition as in (Chiang et al., 2019). Product: ... based on a different preprocessing and split by Hu et al. (2020). Amazon2M: ... We use the same dataset and partition as in (Chiang et al., 2019). |
| Hardware Specification | No | The paper does not explicitly state the specific hardware, such as GPU or CPU models, used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Cluster GCN s tensorflow implementation' but does not specify a version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | As for architecture, on all datasets, we consider a 4-layer GCN model with hidden dimensions 512, 256, 512, and 400 for Product, Arxiv, Reddit, and Amazon2M, respectively, and the mean aggregator from Hamilton et al. (2017). |