Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
Authors: Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four GCIL benchmarks show that i) our task prototype-based method can achieve 100% task ID prediction accuracy on all four datasets, ii) our GCIL model significantly outperforms state-of-the-art competing methods by at least 18% in average CIL accuracy, and iii) our model is fully free of forgetting on the four datasets. |
| Researcher Affiliation | Academia | 1 AAII, University of Technology Sydney, Australia 2 School of Computing and Information Systems, Singapore Management University, Singapore 3 Department of Computer Science, University of Illinois at Chicago, USA |
| Pseudocode | Yes | C Algorithm |
| Open Source Code | Yes | Code is available at https://github.com/mala-lab/TPP. |
| Open Datasets | Yes | Following the GCL performance benchmark [39], four large public graph datasets are employed, including Cora Full [19], Arxiv [9], Reddit [6] and Products [9]. |
| Dataset Splits | Yes | Besides, for each class, the proportions of training, validation, and testing are set to be 0.6, 0.2, and 0.2 respectively. |
| Hardware Specification | Yes | Besides, all experiments are conducted on a Linux server with an Intel CPU (Intel Xeon E-2288G 3.7GHz) and a Nvidia GPU (Quadro RTX 6000). |
| Software Dependencies | Yes | The code is implemented with Pytorch (version: 1.10.0), DGL (version: 0.9.1), OGB (version: 1.3.6), and Python 3.8.5. |
| Experiment Setup | Yes | TPP adopts a two-layer SGC [35] as the GNN backbone model with the same hyper-parameters as [42]. For task prototype construction, the number of steps s in Laplacian smoothing is set to 3 by default. The number of tokens in each graph prompt, k, is also set to 3 across the four datasets. For each dataset, we report the average performance with standard deviations after 5 independent runs with different random seeds. ... Specifically, the hidden dimension is set to 256 for all methods. The number of training epochs of each graph learning task is 200 with Adam as the optimizer and the learning rate is set to 0.005 by default. For graph contrastive learning, the probabilities of edge removal and attribute masking are set to 0.2 and 0.3 respectively for all datasets. Besides, the learning rate is set to 0.001 with Adam optimizer, the training epochs are set to 200 and the temperature τ is 0.5 for all datasets. |