Triplet Enhanced AutoEncoder: Model-free Discriminative Network Embedding
Authors: Yao Yang, Haoran Chen, Junming Shao
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical results on three public datasets (i.e, Cora, Citeseer and Blog Catalog) show that TEA is stable and achieves state-of-the-art performance compared with both supervised and unsupervised network embedding approaches on various percentages of labeled data. |
| Researcher Affiliation | Academia | Yao Yang , Haoran Chen and Junming Shao Data Mining Lab, University of Electronic Science and Technology of China {yaoyang, haorchen}@std.uestc.edu.cn, junmshao@uestc.edu.cn |
| Pseudocode | No | The paper describes the model architecture and optimization process but does not include a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The source code can be obtained from https://github.com/yybeta/TEA. |
| Open Datasets | Yes | Cora1: This is a citation network of academic papers... 1http://www.cs.umd.edu/ sen/lbc-proj/data/cora.tgz; Citeseer2: This is another research paper set... 2http://www.cs.umd.edu/ sen/lbc-proj/data/citeseer.tgz; Blog Catalog3: This is a network of social relationships between users on the Blog Catalog website... 3http://socialcomputing.asu.edu/datasets/Blog Catalog3 |
| Dataset Splits | Yes | For the node classification task, we adopted micro-F1 and macro-F1... Here four-fold cross-validation is applied... We increase the labeled ratio from 20% to 80%, to compare the performance of different algorithms. |
| Hardware Specification | No | The paper mentions training models but does not provide specific hardware details (e.g., CPU/GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions using Adam optimizer and Lib Linear, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The dimension of each layer is listed in Table 1. The hyper-parameters of α and β are tuned by using grid search on the validation set, the sensitivity is further evaluated in Section 4.5. The models were trained for 20 epochs with Adam optimizer. We set the representation dimension to 100 for baseline algorithms, same as our model. The parameters in baselines are tuned. For Gra Rep, we set the maximum matrix transition step K = 5 for Blog Catalog, K = 3 for Cora and Citeseer. For node2vec, we set the window size as 10, the walk length as 80, the walk per vertex as 20, both in-out and return hyper-parameters are set to 0.25. For DNGR, we set the neural network structures the same as TEA. For MMDW, we set the gradient balance parameter η = 10 2. |