Disentangled Graph Convolutional Networks
Authors: Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically assess the efficacy of Disen GCN on several node-related tasks, and analyze its behavior on synthetic graphs to gain further insight. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China. |
| Pseudocode | Yes | Algorithm 1 The proposed Disen Conv layer, with K channels. |
| Open Source Code | No | The paper does not provide a direct link to a code repository or explicitly state that the source code is released. |
| Open Datasets | Yes | We conduct our experiments on six real-world graphs, whose statistics are listed in Table 1. Citeseer, Cora, and Pubmed (Sen et al., 2008) are for semi-supervised node classification. ... Blog Catalog (Tang & Liu, 2009), PPI (Breitkreutz et al., 2008; Grover & Leskovec, 2016), POS (Grover & Leskovec, 2016) are for multi-label node classification. |
| Dataset Splits | Yes | The rest of the nodes are split equally to form a validation set and a test set. We follow the experiment protocol established by the previous works (Yang et al., 2016; Kipf & Welling, 2017; Veliˇckovi c et al., 2018) strictly, and use the same dataset splits as them. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like Hyperopt and Adam for optimization and hyperparameter tuning but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Hyper-parameters Let d be the output dimension of a graph neural network s first layer. In the semi-supervised classification tasks, we follow GAT and use d = 64. In the multi-label classification tasks, we follow node2vec and use d = 128... We set T = 7. We set τ = 1... Specifically, we run hyperopt for 200 trials for each setting, with the hyper-parameter search space specified as follows: the learning rate loguniform[e 8, 1], the l2 regularization term loguniform[e 10, 1], dropout rate {0.05, 0.10, . . . , 0.95}, the number of layers L {1, 2, . . . , 6}, the number of channels used by the first layer K(1) {4, 8, . . . , 32}, and K(l+1) K(l) = K {0, 2, . . . , 8}. |