Learning on Large Graphs using Intersecting Communities
Authors: Ben Finkelshtein, Ismail Ceylan, Michael Bronstein, Ron Levie
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our methods with the following experiments: Runtime analysis (Section 6.1): We report the forward pass runtimes of ICGu-NN and GCN [30], empirically validating the theoretical advantage of the former. We further extend this analysis in Appendices F.7 and F.8. Node classification (Appendix F.1): We evaluate our method on real-world node classification datasets [43, 45, 36], observing that the model performance is competitive with standard approaches. Node classification using Subgraph SGD (Section 6.2 and Appendix F.3): We evaluate our subgraph SGD method (Section 4.3) to identify the effect of sampling on the model performance on the tolokers and Flickr datasets [43, 65]. We find the model s performance to be robust on tolokers and state-of-the-art on Flickr. Spatio-temporal tasks (Section 6.3): We evaluate ICGu-NN on real-world spatio-temporal tasks [35] and obtain competitive performance to domain-specific baselines. Comparison to graph coarsening methods (Appendix F.2): We provide an empirical comparison between ICG-NNs and a variety of graph coarsening methods on the Reddit [23] and Flickr [65] datasets, where ICG-NNs achieve state-of-the-art performance. Additional experiments: We perform an ablation study over the number of communities (Appendix F.4) and the choice of initialization in Section 4.2 (Appendix F.6). We moreover experimentally demonstrate a positive correlation between the Frobenius error and cut norm error as hinted by Theorem 3.1 (Appendix F.5), and perform a memory allocation analysis (Appendix F.9). |
| Researcher Affiliation | Collaboration | Ben Finkelshtein University of Oxford Ismail Ilkan Ceylan University of Oxford Michael Bronstein University of Oxford / AITHYRA Ron Levie Technion Israel Institute of Technology |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | All our experiments are run on a single NVidia L40 GPU. We made our codebase available online: https://github.com/benfinkelshtein/ICGNN. |
| Open Datasets | Yes | We evaluate our method on real-world node classification datasets [43, 45, 36]... We evaluate ICGu-NN on real-world spatio-temporal tasks [35]... We evaluate ICG-NN and ICGu-NN on the large graph datasets Flickr [65] and Reddit [23]. |
| Dataset Splits | Yes | We segment the datasets into windows of 12 time steps and train the models to predict the subsequent 12 observations. For all datasets, these windows are divided sequentially into 70% for training, 10% for validation, and 20% for testing. We report the mean absolute error (MAE) and standard deviation averaged over the forecastings. |
| Hardware Specification | Yes | All our experiments are run on a single NVidia L40 GPU. We made our codebase available online: https://github.com/benfinkelshtein/ICGNN. |
| Software Dependencies | No | The paper mentions software components like GCN [30], DCRNN [35], Graph Wave Net [60], AGCRN [8], Adam optimizer, and GRU, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | Additionally, we use the Adam optimizer and detail all hyperparameters in Appendix I. ... In Tables 10 to 12, we report the hyper-parameters used in our real-world node-classification, spatio-temporal and graph coarsening benchmarks. |