Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
Authors: Yuhang Yao, Carlee Joe-Wong4608-4616
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We finally demonstrate that the proposed architectures perform well on real data compared to state-of-the-art graph clustering algorithms. Experimental results on real and simulated datasets that show our proposed RNN-GCN architectures outperform state-of-the-art graph clustering algorithms. |
| Researcher Affiliation | Academia | Yuhang Yao, Carlee Joe-Wong Carnegie Mellon University {yuhangya,cjoewong}@andrew.cmu.edu |
| Pseudocode | Yes | Algorithm 1: RNNGCN |
| Open Source Code | Yes | The code of all methods and datasets are publicly available4. 4https://github.com/Interpretable Clustering/Interpretable Clustering |
| Open Datasets | Yes | We conducted experiments on five real datasets, as shown in Table 1, which have the properties shown in Table 2. ... DBLP-E dataset is extracted from the computer science bibliography website DBLP1, which provides open bibliographic information... 1https://dblp.org ... Reddit dataset is generated from Reddit2... 2https://www.reddit.com/ ... Brain dataset is generated from functional magnetic resonance imaging (f MRI) data3. 3https://tinyurl.com/y4hhw8ro |
| Dataset Splits | Yes | We divide each dataset into 70% training/ 20% validation/ 10% test points. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Re LU' but does not specify version numbers for any programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Each method uses two hidden Graph Neural Network layers (GCN, GAT, Graph Sage, etc.) with the layer size equal to the number of classes in the dataset. We add a dropout layer between the two layers with dropout rate 0.5. We use the Adam optimizer with learning rate 0.0025. Each method is trained with 500 iterations. |