AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
Authors: Ke Sun, Zhanxing Zhu, Zhouchen Lin
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments demonstrate the consistent state-of-the-art prediction performance on graphs across different label rates and the computational advantage of our approach Ada GCN 1. |
| Researcher Affiliation | Academia | Ke Sun Zhejiang Lab Key Lab. of Machine Perception (Mo E), School of EECS, Peking University ajksunke@pku.edu.cn Zhanxing Zhu* Beijing Institute of Big Data Research, Beijing, China zhanxing.zhu@pku.edu.cn Zhouchen Lin Key Lab. of Machine Perception (Mo E), School of EECS, Peking University Pazhou Lab, Guangzhou, China zlin@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 Ada GCN based on SAMME.R Algorithm |
| Open Source Code | Yes | Code is available at https://github.com/datake/Ada GCN. |
| Open Datasets | Yes | We select five commonly used graphs: Cite Seer, Cora-ML (Bojchevski & G unnemann, 2018; Mc Callum et al., 2000), Pub Med (Sen et al., 2008), MS-Academic (Shchur et al., 2018) and Reddit. |
| Dataset Splits | Yes | We choose a training set of a fixed nodes per class, an early stopping set of 500 nodes and test set of remained nodes. Each experiment is run with 5 random initialization on each data split, leading to a total of 100 runs per experiment. On a standard setting, we randomly select 20 nodes per class. For the two different label rates on each graph, we select 6, 11 nodes per class on citeseer, 8, 16 nodes per class on Cora-ML, 7, 14 nodes per class on Pubmed and 8, 15 nodes per class on MS-Academic dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using certain approaches (e.g., 'the implementation of Ada GCN is adapted from APPNP') but does not specify any software names with version numbers. |
| Experiment Setup | Yes | For all GCN-based approaches, we use the same hyper-parameters in the original paper: learning rate of 0.01, 0.5 dropout rate, 5 × 10−4 L2 regularization weight, and 16 hidden units. For Fast GCN, we adopt the officially released code to conduct our experiments. PPNP and APPNP are adapted with best setting: K = 10 power iteration steps for APPNP, teleport probability γ = 0.1 on Cora-ML, Citeseer and Pubmed, γ = 0.2 on Ms-Academic. In addition, we use two layers with h = 64 hidden units and apply L2 regularization with λ = 5 × 10−3 on the weights of the first layer and use dropout with dropout rate d = 0.5 on both layers and the adjacency matrix. The early stopping criterion uses a patience of p = 100 and an (unreachably high) maximum of n = 10000 epochs.The implementation of Ada GCN is adapted from PPNP and APPNP. Corresponding patience p = 300 and n = 500 in the early stopping of Ada GCN. |