Zero-shot Node Classification with Graph Contrastive Embedding Network
Authors: Wei Ju, Yifang Qin, Siyu Yi, Zhengyang Mao, Kangjie Zheng, Luchen Liu, Xiao Luo, Ming Zhang
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments indicate that our Graph CEN significantly outperforms the state-of-the-art approaches on multiple challenging benchmark datasets. ... 4 Experiments |
| Researcher Affiliation | Academia | 1School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University 2School of Statistics and Data Science, Nankai University 3Department of Computer Science, University of California, Los Angeles |
| Pseudocode | Yes | Algorithm 1: Optimization Algorithm of Graph CEN Input : Graph data G = (V, E, X, A), classes semantic descriptions matrix S, total classe set C, seen classes Cs, trainable weight matrix W in GNN, parameter matrix Θ = (Θ 1 , Θ 2 ) in two projection heads, temperature parameter τ and contrastive parameter β Output: Class assignments for unlabeled nodes from unseen class Cu Construct the class affinity graph GA by Eq. 2; Initializing trainable weight matrix W and parameter matrix Θ; while not done do Compute the joint information matrix G by Eq. 4; Sample a mini-batch of nodes from G; Sample one augmentation from Section 2; Compute node-level contrastive loss LNCL by Eq. 6; Compute class-level contrastive loss LCCL by Eq. 8; Compute the supervised loss LCE by Eq. 10; Update parameters W and Θ by gradient descent to minimize L by Eq. 11 end |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | For comprehensive comparisons, we conduct extensive experiments on three real-world citation datasets, which are Cora (Mc Callum et al., 2000), Citeseer (Giles et al., 1998), C-M10M (Pan et al., 2016) and a large-scale dataset ogbn-arxiv (Wang et al., 2020). |
| Dataset Splits | Yes | The specific class labels of the datasets are displayed in Table 1. For the zero-shot node classification, we follow the same seen/unseen class split settings introduced in (Wang et al., 2021b) to make a fair comparison as shown in Table 1. ... Table 1: The data split and class information of three citation datasets. Dataset Classes Class Split I Class Split II Class Labels [Train/Val/Test] [Train/Val/Test] Cora 7 [3/0/4] [2/2/3] Neural Network, Rule Learning, Reinforcement Learning, Probabilistic Methods, Theory, Genetic Algorithms, Cased based |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | Besides, the framework is trained using Adam optimizer (Kingma & Ba, 2014). This mentions an optimizer but lacks a specific version number for it or other key software components/libraries. |
| Experiment Setup | Yes | For our Graph CEN, we adopt the grid search for the parameters under the class split I, and determine the optimal hyper-parameters by the validation classes under the class split II. The space of hyper-parameters is carefully selected as follows: the learning rate {0.0005, 0.001, 0.005, 0.01}, the number of hidden units {32, 64, 128, 256}, the temperature parameter τ {0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0}, the contrastive parameter β {0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0}. Besides, the framework is trained using Adam optimizer (Kingma & Ba, 2014). |