Bundle Neural Network for message diffusion on graphs
Authors: Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael Bronstein
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our claims with formal analysis and synthetic experiments. Empirically, Bu NNs perform strongly on heterophilic and long-range tasks, which demonstrates their robustness on a diverse range of challenging real-world tasks. In summary, our contributions are the following: [...] We show that Bu NNs perform well on heterophilic and long-range tasks, for instance, achieving a new state-of-the-art result on the Peptides-func dataset (Section 6.2). |
| Researcher Affiliation | Collaboration | Jacob Bamberger1 Federico Barbero1 Xiaowen Dong1 Michael Bronstein1,2 1University of Oxford 2AITHYRA EMAIL |
| Pseudocode | Yes | Algorithm 1 Taylor expansion implementation of a Bu NN layer [...] Algorithm 2 Spectral implementation of a Bu NN layer |
| Open Source Code | Yes | All code can be found at https://github.com/jacobbamberger/Bu NN |
| Open Datasets | Yes | We evaluate Bu NNs on the Long Range Graph Benchmark (Dwivedi et al., 2022) and the heterophilic tasks from Platonov et al. (2023). |
| Dataset Splits | Yes | We use 100 samples for both training and testing. [...] For peptides-func and peptides-struct we use a fixed parameter budget of 500k as in Dwivedi et al. (2022). We fix hyper-parameters to be the best GCN hyper-parameters from T onshoff et al. (2023) [...] For the heterophilic graphs we use the source code from Platonov et al. (2023) in which we add our layer definition. |
| Hardware Specification | Yes | All experiments were ran on a cluster using NVIDIA A10 (24 GB) GPUs, each experiment using at most 1 GPU. Each machine in the cluster has 64 cores of Intel(R) Xeon(R) Gold 6326 CPU at 2.90GHz, and 500GB of RAM available. |
| Software Dependencies | No | We use the Pytorch package from (?). [...] We use Adam optimizer with 10 3 learning rate. |
| Experiment Setup | Yes | Dropout 0.1 [...] Optimizer: Adam W with a cosine annealing learning rate schedule and linear warmup. [...] Batch size 200 [...] For the heterophilic graphs we use [...] GELU activation functions, the Adam optimizer with learning rate 3 10 5, and train all models for 2000 epochs and select the best epoch based on the validation set. Tables 5, 6, and 7 provide detailed hyperparameter grids and best values. |