Probabilistic Graph Rewiring via Virtual Nodes
Authors: Chendi Qian, Andrei Manolache, Christopher Morris, Mathias Niepert
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we validate our approach by showcasing its ability to mitigate under-reaching and oversquashing effects, achieving state-of-the-art performance across multiple graph datasets. |
| Researcher Affiliation | Collaboration | 1Computer Science Department, RWTH Aachen University, Germany 2Computer Science Department, University of Stuttgart, Germany 3IMPRS-IS 4Bitdefender, Romania |
| Pseudocode | No | The paper describes the message-passing architecture using descriptive text and mathematical equations in Section 3 but does not include a formal pseudocode block or algorithm figure. |
| Open Source Code | Yes | An open repository of our code can be accessed at https://github.com/chendiqian/ IPR-MPNN. |
| Open Datasets | Yes | Our results demonstrate that IPR-MPNNs effectively account for long-range relationships, achieving state-of-the-art performance on the PEPTIDES and PCQM-CONTACT datasets, as detailed in Table 2. Notably, on the PCQM-CONTACT link prediction tasks, IPR-MPNNs outperform all other candidates across three measurement metrics outlined in Tönshoff et al. [2023]. For QM9, we show in Table 1 that IPR-MPNNs greatly outperform similar methods, obtaining the best results on 12 of 13 target properties. On ZINC and OGB-MOLHIV, we outperform similar MPNNs and graph transformers, namely GPS Rampášek et al. [2022] and SAT [Chen et al., 2022a], obtaining state-of-the-art results; see Table 4. For the TUDATASET collection, we achieve the best results on four of the five molecular datasets; see Table A9. |
| Dataset Splits | Yes | We use the official dataset splits when available. Notably, for the TUDATASET, WEBKB datasets [Craven et al., 1998] and heterophilic datasets proposed in Platonov et al. [2023], we perform a 10-Fold Cross-Validation and report the average validation performance, similarly to the other methods that we compare with. |
| Hardware Specification | Yes | All experiments were performed on a mixture of A10, A100, A5000, and RTX 4090 NVIDIA GPUs. For each run, we used at most eight CPUs and 64 GB of RAM. |
| Software Dependencies | No | The paper mentions that 'All datasets are available through the interface of PyTorch Geometric,' but it does not specify version numbers for PyTorch Geometric or any other software dependencies. |
| Experiment Setup | Yes | In all of our real-world experiments, we use two virtual nodes with a hidden dimension twice as large as the base nodes. We randomly initialize the features of the virtual nodes. For the upstream and downstream models, we do a hyperparameter search; see Table A5. We use RWSE and Lap PE positional encodings [Dwivedi et al., 2022a] for all of our experiments as additional node features... We optimize the network using Adam Kingma and Ba [2015] with a cosine annealing learning rate scheduler. |