On Graph Neural Networks versus Graph-Augmented MLPs
Authors: Lei Chen, Zhengdao Chen, Joan Bruna
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | From the results in Table 1, we see that indeed, the number of equivalence classes induced by GA-MLP-A is smaller than that by GNNs, with the highest relative difference occurring at K = 2. From Table 2, we see that GIN significantly outperforms GA-MLPs in both training and testing on both graphs, consistent with the theoretical result in Proposition 6 that GNNs can count attributed walks while GA-MLPs cannot. |
| Researcher Affiliation | Academia | Lei Chen , Zhengdao Chen Courant Institute of Mathematical Sciences New York University, New York, NY {lc3909, zc1216}@nyu.edu Joan Bruna Courant Institute of Mathematical Sciences Center for Data Science New York University, New York, NY bruna@cims.nyu.edu |
| Pseudocode | No | The paper describes methods and theoretical arguments but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | Yes | Code available at https://github.com/leichen2018/GNN_vs_GAMLP. |
| Open Datasets | Yes | We take graphs from the Cora dataset (with node features removed) as well as generate a random regular graph (RRG) with 1000 nodes and the node degree being 6. Cora Citeseer Pubmed |
| Dataset Splits | No | The number of nodes for training and testing is split as 1000/1708 for the Cora graph and 300/700 for the random regular graph. Only training and testing splits are mentioned, no explicit validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The model is trained with the Adam optimizer (Kingma & Ba, 2014) with learning rate selected from {0.1, 0.02, 0.01, 0.005, 0.001}. We use instance normalization (Ulyanov et al., 2016). These mention software tools but do not provide specific version numbers required for reproducibility. |
| Experiment Setup | Yes | The number of hidden dimensions is searched in {8, 16, 32, 64, 256}. The model is trained with the Adam optimizer (Kingma & Ba, 2014) with learning rate selected from {0.1, 0.02, 0.01, 0.005, 0.001}. The highest order of operators is searched in {30, 60, 120}. The number of hidden dimensions is searched in {10, 20}. |