Network Embedding with Dual Generation Tasks
Authors: Jie Liu, Na Li, Zhicheng He
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show that our model yields a significant performance gain compared to the state-of-the-art NE methods. Moreover, our model has an interesting and useful byproduct, that is, a component of our model can generate texts, which is potentially useful for many tasks. |
| Researcher Affiliation | Academia | Jie Liu , Na Li and Zhicheng He Nankai University, Tianjin, China jliu@nankai.edu.cn, nali nku@163.com, hezhicheng@mail.nankai.edu.cn |
| Pseudocode | No | The paper describes the model in detail with text and figures, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Our DGENE model is evaluated on two real-world scientific paper citation networks. Cora contains 2211 papers from 7 categories, and there are 5214 citation links between them. Citeseer contains 4610 papers which are divided into 10 categories. There are 5923 links between these papers. |
| Dataset Splits | No | The percentages of labeled nodes in classification are set to 10%, 30%, 50%, 70%, and 90%. The paper describes the percentage of labeled nodes used for training the downstream classifier, but does not provide explicit train/validation/test splits for the main datasets used in the embedding process, nor a separate validation set percentage for the classification task. |
| Hardware Specification | Yes | With a NVIDIA Ge Force GTX 1080Ti GPU, the actual running time of an epoch is about 4 minutes on Citeseer dataset and 7 minutes on Cora dataset. |
| Software Dependencies | No | The paper mentions general techniques and models like Bi-LSTM, CNN, and logistic regression, but does not specify any software libraries or their version numbers. |
| Experiment Setup | Yes | For both encoder and both decoder layers in dual models, we apply dropout with probability p = 0.2. For both datasets, we set the matrixs U and V randomly initializated, the hidden dimension of encoders k = 300, the hidden dimension of decoders hd = 600, ku = 400, kn = 300, kh = 600, ki = 400, kk = [2, 3, 4, 5]. Besides, we set M = γ = 100, Z = 22, 110 on Cora dataset and M = γ = 48, Z = 46, 100 on Citeseer dataset. |