Serving Graph Compression for Graph Neural Networks
Authors: Si Si, Felix Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 {sisidaisy, felixyu, ankitsrawat, sanjivk}@google.com {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). |