Active Discriminative Network Representation Learning
Authors: Li Gao, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, Yue Hu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB. |
| Researcher Affiliation | Academia | Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China Centre for Artiļ¬cial Intelligence, University of Technology Sydney, Australia Department of Computing, Macquarie University, Sydney, Australia School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | Yes | Algorithm 1: The proposed ANRMAB algorithm. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the proposed method nor does it include a link to a code repository. |
| Open Datasets | Yes | We consider three public citation network data sets1, Citeseer, Cora and Pubmed [Sen et al., 2008]... 1https://linqs.soe.ucsc.edu/data |
| Dataset Splits | Yes | To ensure that the performance variation in the experiments is due to different AL query strategies and their selections, we randomly sample 500 labeled nodes from the non-testing nodes for validation, which is repeated for 10 times. |
| Hardware Specification | Yes | All the experiments are conducted on a Linux system with Intel(R) Core(TM) i7-2600 CPU @3.40GHz*8 and 10G memory. |
| Software Dependencies | No | The paper mentions the use of Adam optimizer, but it does not specify version numbers for any programming languages, libraries, or frameworks (e.g., Python, TensorFlow, PyTorch) that would be needed for reproducibility. |
| Experiment Setup | Yes | The discriminative network representation learning in all the compared methods are trained using Eq. (8) for a maximum of 600 epochs using Adam [Kingma and Ba, 2014] with a learning rate of 0.01, a hidden layer size of 32 and early stopping with a window size of 10. |