Dual Contrastive Graph-Level Clustering with Multiple Cluster Perspectives Alignment
Authors: Jinyu Cai, Yunhe Zhang, Jicong Fan, Yali Du, Wenzhong Guo
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the superiority of DCGLC over the state-of-the-art baselines on numerous graph benchmarks. In this section, we conduct a series of evaluations to demonstrate the superiority of the proposed DCGLC. |
| Researcher Affiliation | Academia | Jinyu Cai1 , Yunhe Zhang2,3 , Jicong Fan2,3 , Yali Du4 and Wenzhong Guo5 1Institute of Data Science, National University of Singapore, Singapore 2Shenzhen Research Institute of Big Data, Shenzhen, China 3School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China 4Department of Informatics, King s College London, United Kingdom 5College of Computer and Data Science, Fuzhou University, China |
| Pseudocode | Yes | Algorithm 1 Training flows of DCGLC. |
| Open Source Code | Yes | Code is available at: https://github.com/wownice333/DCGLC |
| Open Datasets | Yes | We consider three types of graph benchmark datasets to evaluate the clustering performance, including seven molecule datasets (MUTAG, BZR, PTC-MR, PTCMM, COX2, ER MD, and AIDS), three biological datasets (DD, PROTEINS and ENZYMES), and two social network datasets (IMDB-BINARY and REDDIT-MULTI-5K). Details of these benchmarks refer to Appendix A. |
| Dataset Splits | No | The paper mentions using a batch size of 64 and training for 300 epochs, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It makes no mention of hardware at all in relation to its experiments. |
| Software Dependencies | No | The paper mentions software components like '4-layer GIN' as the backbone network, 'MLP-based feature projection head and cluster projection head', and 'Adam as the optimizer'. However, it does not provide specific version numbers for any of these libraries, frameworks, or the programming language used. |
| Experiment Setup | Yes | We employ a 4-layer GIN [Xu et al., 2019] as the backbone network for the proposed DCGLC, with the aggregated dimension set to 16. We utilize an MLP-based feature projection head and cluster projection head, with the dimension of the latent layer and the clustering embedding layer both set to 10. The number of clusters is set to the number of categories in the dataset, the batch size is fixed to 64, and the training epoch is set to 300 for all datasets. We use Adam as the optimizer and follow the setting in [You et al., 2021] to augment graphs automatically in each epoch. |