Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Personalized Layer Selection for Graph Neural Networks
Authors: Kartik Sharma, Vineeth Rakesh, Yingtong Dou, Srijan Kumar, Mahashweta Das
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on 10 datasets and 3 different GNNs show that we significantly improve the node classification accuracy of GNNs in a plug-and-play manner. We also find that using variable layers for prediction enables GNNs to be deeper and more robust to poisoning attacks. |
| Researcher Affiliation | Collaboration | Kartik Sharma EMAIL Georgia Institute of Technology; Vineeth Rakesh EMAIL Visa Research; Yingtong Dou EMAIL Visa Research; Srijan Kumar EMAIL Georgia Institute of Technology; Mahashweta Das EMAIL Visa Research |
| Pseudocode | Yes | Algorithm 1 describes the training steps in more detail. |
| Open Source Code | No | Code will be open-sourced after publication. |
| Open Datasets | Yes | We consider 4 standard homophilic co-citation network datasets Cora, Citeseer, Pubmed (Kipf & Welling, 2016) and ogbn-arxiv (ogba) (Hu et al., 2020), where each node represents a paper that is classified based on its topic area. We also used 6 heterophilic datasets Actor, Chameleon, Squirrel, Cornell, Wisconsin, Texas (Pei et al., 2020). |
| Dataset Splits | Yes | Following Pei et al. (2020), we evaluate the models on 10 different random train-val-test splits for all the datasets except ogba, where we used the standard OGB split. |
| Hardware Specification | Yes | All the experiments were conducted on Python 3.8.12 on a Ubuntu 18.04 PC with an Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz processor, 512 GB RAM, and Tesla V100-SXM2 32 GB GPUs. |
| Software Dependencies | No | All the experiments were conducted on Python 3.8.12 on a Ubuntu 18.04 PC with an Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz processor, 512 GB RAM, and Tesla V100-SXM2 32 GB GPUs. The paper mentions using GNNs from 'pytorch-geometric.readthedocs.io' but does not specify the version of PyTorch Geometric or PyTorch itself. |
| Experiment Setup | Yes | All the models were trained using an Adam optimizer for 500 epochs with the initial learning rate tuned between {0.01, 0.001}. The best-trained model was chosen using the validation accuracy and in the case of multiple splits, the mean validation accuracy across splits. |