Fast and Deep Graph Neural Networks
Authors: Claudio Gallicchio, Alessio Micheli3898-3905
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experimental results, we show that even without training of the recurrent connections, the architecture of small deep GNN is surprisingly able to achieve or improve the state-of-the-art performance on a significant set of tasks in the field of graphs classification. |
| Researcher Affiliation | Academia | Claudio Gallicchio, Alessio Micheli Department of Computer Science, University of Pisa Largo B. Pontecorvo, 3. 56127 Pisa, Italy {gallicch, micheli}@di.unipi.it |
| Pseudocode | No | The paper describes the model and process steps in text and equations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is made available online2. 2https://sites.google.com/site/cgallicch/FDGNN/code |
| Open Datasets | Yes | All the used datasets are publicly available online (Kersting et al. 2016). |
| Dataset Splits | Yes | The performance on the graph classification tasks was assessed in terms of accuracy, and it was evaluated through a process of stratified 10-fold cross validation. For each fold, the FDGNN hyper-parameter configuration was chosen by model selection, by means of a nested level of stratified 10-fold cross validation applied on the corresponding training samples. |
| Hardware Specification | Yes | a MATLAB implementation of FDGNN, running on a system with Intel Xeon Processor E5-263L v3 with 168 GB of RAM. |
| Software Dependencies | No | The paper states 'a MATLAB implementation of FDGNN' but does not provide specific version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | We adopted a simple general setting, where all the hidden layers of the architecture in the graph embedding component shared the same values of the hyper-parameters, i.e., for i 1 we set: number of neurons H(i) = H and effective spectral radius ρ(i) = ρ; for i > 1 we set the inter-layer scaling ω(i) = ω. In particular, we set the number of neurons in each hidden layer to H = 50 for all datasets except for NCI1 and COLLAB, for which we used H = 500. We implemented very sparse weight matrices with C = 1... Values of ρ, ω(1) and ω were explored in the same range (0, 1). The Tikhonov regularizer for training the readout was searched in a log-scale grid in the range 10 8 103. For the graph embedding convergence process in each layer we used a threshold of ϵ = 10 3, and maximum number of iterations ν = 50. The projection dimension for the readout was set to twice the number of neurons in the last hidden layer, i.e., P = 2 H. |