A Divide and Conquer Framework for Distributed Graph Clustering
Authors: Wenzhuo Yang, Huan Xu
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and real-world datasets demonstrate the efficiency and effectiveness of our framework. |
| Researcher Affiliation | Academia | Wenzhuo Yang A0096049@NUS.EDU.SG Department of Mechanical Engineering, National University of Singapore, Singapore 117576Huan Xu MPEXUH@NUS.EDU.SG Department of Mechanical Engineering, National University of Singapore, Singapore 117576 |
| Pseudocode | Yes | Algorithm 1 Large Graph Clustering and Algorithm 2 Build a Fused Graph are presented. |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for the described methodology is openly available. |
| Open Datasets | Yes | We now evaluate DC on three real-world datasets, namely, MNIST (Le Cun et al., 1995), Arxiv1 and DBLP2. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts for training, validation, or test sets). |
| Hardware Specification | Yes | The experiments are conducted on a desktop PC with an i7 3.4GHz CPU and 4GB memory. |
| Software Dependencies | No | The paper mentions implementing algorithms in Python but does not provide specific version numbers for Python or any relevant libraries. |
| Experiment Setup | Yes | We set l = 1, t = (p + q)/2 and T = 10 in the following experiments... Parameter λ and c C in (1) are set to 1.6 and 5, respectively. |