Attributed Graph Clustering: A Deep Attentional Embedding Approach
Authors: Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.The experimental results show that our algorithm outperforms state-of-the-art graph clustering methods. |
| Researcher Affiliation | Academia | 1Centre for Artificial Intelligence, University of Technology Sydney, Australia 2Faculty of IT, Monash University, Australia {chun.wang-1, ruiqi.hu}@student.uts.edu.au, shirui.pan@monash.edu, {guodong.long, jing.jiang, chengqi.zhang}@uts.edu.au |
| Pseudocode | Yes | Algorithm 1 Deep Attentional Embedded Graph Clustering |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of its source code. |
| Open Datasets | Yes | We used three standard citation networks widely-used for assessment of attributed graph analysis in our experiments, summarized in Table 1. Publications in the datasets are categorized by the research sub-fields. Cora 2,708 1,433 7 5,429 3,880,564 Citeseer 3,327 3,703 6 4,732 12,274,336 Pubmed 19,717 500 3 44,338 9,858,500 |
| Dataset Splits | No | The paper mentions using benchmark datasets (Cora, Citeseer, Pubmed) but does not specify the train/validation/test splits used for these datasets within the text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper does not specify the version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For our method, we set the clustering coefficient γ to 10. We consider second-order neighbors and set M = (B+B2)/2. The encoder is constructed with a 256-neuron hidden layer and a 16-neuron embedding layer for all datasets. |