GALOPA: Graph Transport Learning with Optimal Plan Alignment
Authors: Yejiang Wang, Yuhai Zhao, Daniel Zhengkui Wang, Ling Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental findings include: (i) The plan alignment strategy significantly outperforms the counterpart using the transport distance; (ii) The proposed model shows superior performance using only node attributes as calibration signals, without relying on edge information; (iii) Our model maintains robust results even under high perturbation rates; (iv) Extensive experiments on various benchmarks validate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Northeastern University, China 2 Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, China 3 Info Comm Technology Cluster, Singapore Institute of Technology, Singapore |
| Pseudocode | No | The paper does not include a clearly labeled pseudocode or algorithm block. The methodology is described in prose within Section 4. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | For node classification, we evaluate the performance of using the pretraining representations on 7 benchmark graph datasets, namely, CORA, CITESEER, PUBMED [25] and Wiki-CS, Amazon-Computers, Amazon-Photo, and Coauthor-CS [47]. For graph classification, we follow GRAPHCL [72] to perform evaluations on 6 graph classification data NCI1, PROTEINS, DD, MUTAG, COLLAB, and IMDB-B from TUDataset [36]. |
| Dataset Splits | Yes | For the graphs (nodes) datasets, we randomly split the data, where 80%/10%/10% (10%/10%/80%) of graphs (nodes) are selected for the training, validation, and test set, respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., specific GPU or CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions that the model is "implemented with Pytorch Geometric [13] and Deep Graph Library [63]" but does not specify version numbers for these software components or any other dependencies. |
| Experiment Setup | Yes | In the experiments, we use the Adam optimizer [23] with learning rate is tuned in {0.0001, 0.001, 0.01}. We conduct the experiment with the trade-off parameter ρ and σ, the parameter C of SVM, batch size in the sets {10 3, 10 2, . . . , 102, 103}, {0, 0.1, . . . , 0.9, 1}, {10 3, . . . , 103}, {16, 64, 128, 256, 512}, respectively. To perform graph augmentation, we use 4 types of operations: Edge Perturbation, Feature Masking, Node Dropping, and Graph Sampling. |