Agent-based Graph Neural Networks
Authors: Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide an extensive theoretical analysis of Agent Net: We show that the agents can learn to systematically explore their neighborhood and that Agent Net can distinguish some structures that are even indistinguishable by 2-WL. Moreover, Agent Net is able to separate any two graphs which are sufficiently different in terms of subgraphs. We confirm these theoretical results with synthetic experiments on hard-to-distinguish graphs and real-world graph classification tasks. In both cases, we compare favorably not only to standard GNNs but also to computationally more expensive GNN extensions. |
| Researcher Affiliation | Collaboration | Karolis Martinkus1, P al Andr as Papp2, Benedikt Schesch1, Roger Wattenhofer1 1ETH Zurich 2Computing Systems Lab, Huawei Zurich Research Center |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block found. The model details are described in paragraph text within Section 3 and Appendix C. |
| Open Source Code | Yes | Code is available at https://github.com/Karolis Mart/Agent Net |
| Open Datasets | Yes | following Papp et al. [59] we use three bioinformatics datasets (MUTAG, PTC, PROTEINS) and two social network datasets (IMDB-BINARY and IMDB-MULTI) [76]. (...) We test this extension on the OGB-Mol HIV molecule classification dataset, which uses edge features [38]. (...) we test our Agent Net on the molecule property regression task on the QM9 dataset [63]. (...) We also test our model on a popular ZINC-12K molecular graph regression dataset [24; 40]. |
| Dataset Splits | Yes | We follow the evaluation setup by Xu et al. [75], as was done by all of the baseline models. We perform a 10-fold cross-validation and report the mean and the standard deviation. (...) We follow the standard evaluation setup proposed by Hu et al. [38]. Similarly to the previous graph classification tasks, we set k to mean number of nodes (n 26), considered number of steps ℓ {8, 16}, batch size {32, 64} and hidden units {64, 128}. We train the model with 10 random seeds for 100 epochs, select the model with the best validation ROC-AUC and report the mean and the standard deviation. |
| Hardware Specification | No | The higher-order methods cannot even train on these large graphs on a 24GB GPU. This is the only mention of hardware, but it lacks specific model or processor details required for a 'Yes' classification. |
| Software Dependencies | No | The model is implemented using Py Torch [60] and Py Torch Geometric [29]. Specific version numbers for these software components are not provided. |
| Experiment Setup | Yes | For Agent Net in all of the tasks, we use Adam W optimizer [47] with a weight decay of 0.1. We set the initial learning rate to 10 4 and decay it over the whole training to 10 11 using a cosine schedule. We also clip the global gradient norm to 1. In all of the cases, Gumbel-Softmax temperature is set to 2 3. (...) we perform a grid search over the batch size {32, 128}, hidden units {32, 64, 128} and number of steps ℓ {8, 16}. |