CONC: Complex-noise-resistant Open-set Node Classification with Adaptive Noise Detection
Authors: Qin Zhang, Jiexin Lu, Xiaowei Li, Huisi Wu, Shirui Pan, Junyang Chen
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate the superiority of our method. We designed experiments to evaluate CONC, focusing primarily on: comparison of open-set classification, robustness analysis, and ablation study. |
| Researcher Affiliation | Academia | 1College of Computer Science and Software Engineering, Shenzhen University, China 2School of Information and Communication Technology, Griffith University, Australia |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The relevant code will be made publicly accessible online. |
| Open Datasets | Yes | To evaluate the performance of the proposed framework for robust open-set node classification, we performed experiments on four widely-used citation network benchmark datasets: Cora, Citeseer, Coauthor-CS, and Ogbn-arxiv. The datasets statistics are presented in the Appendix. |
| Dataset Splits | Yes | Among the known classes, 70% of nodes were allocated for training, 10% for validation, and the remaining 20% for testing. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The CONC framework is implemented using Py Torch, and network optimization leverages adaptive moment estimation with a learning rate of 10-3. |
| Experiment Setup | Yes | For CONC, we use GCN [Kipf and Welling, 2016] as the backbone network for the encoder ϕb, with four hidden layers of 128 dimensions. The feature decoder ψf and the degree decoder ψg are configured with three-layer and four-layer multilayer perceptron with a dimension of 128. To train the open-set node classifier ψc, an additional linear layer is added for it. We employed a grid search to determine the optimal values for thresholds τ, τ1, τ2, exploring the range from 0 to 1 with a step size of 10-1. The CONC framework is implemented using Py Torch, and network optimization leverages adaptive moment estimation with a learning rate of 10-3. Balance parameters γ1 and γ2 were also identified through a grid search, considering values from 10-3 to 101 with a step size of 101. |