TopoGCL: Topological Graph Contrastive Learning
Authors: Yuzhou Chen, Jose Frias, Yulia R. Gel
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive numerical results on biological, chemical, and social interaction graphs show that the new Topological Graph Contrastive Learning (Topo GCL) model delivers significant performance gains in unsupervised graph classification for 8 out of 12 considered datasets and also exhibits robustness under noisy scenarios. |
| Researcher Affiliation | Academia | Yuzhou Chen1, Jose Frias2, Yulia R. Gel3,4 1Department of Computer and Information Sciences, Temple University 2Department of Mathematics, UNAM 3Department of Mathematical Sciences, University of Texas at Dallas 4National Science Foundation yuzhou.chen@temple.edu, frias@ciencias.unam.mx, ygl@utdallas.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of Topo GCL is publicly available at https://github.com/topogclaaai24/Topo GCL.git. |
| Open Datasets | Yes | We validate Topo GCL on unsupervised representation learning tasks using the following 12 real-world graph datasets: (i) 5 chemical compound datasets: NCI1, MUTAG, DHFR, BZR, and COX2, (ii) 4 molecular compound datasets: DD, PROTEINS, PTC MR, and PTC FM, (iii) 2 internet movie databases: IMDBBINARY (IMDB-B) and IMDB-MULTI (IMDB-M), and (iv) 1 Reddit discussion threads dataset: REDDIT-BINARY (REDDIT-B). |
| Dataset Splits | Yes | For all graphs, following the experimental settings of Graph CL (You et al. 2020), we use 10-fold cross validation accuracy as the classification performance (based on a non-linear SVM model, i.e., LIB-SVM (Chang and Lin 2011)) and repeat the experiments 5 times to report the mean and standard deviation. |
| Hardware Specification | Yes | We conduct our experiments on two NVIDIA RTX A5000 GPU cards with 24GB memory. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'LIB-SVM' but does not specify version numbers for these or other key software components like programming languages or deep learning frameworks. |
| Experiment Setup | No | The paper mentions that 'The tuning of Topo GCL on each dataset is done via grid hyperparameter configuration search over a fixed set of choices' and that 'Topo GCL is trained end-to-end by using Adam optimizer,' but it does not specify concrete hyperparameter values such as learning rate, batch size, or number of epochs in the main text. |