Link Prediction via Ranking Metric Dual-Level Attention Network Learning
Authors: Zhou Zhao, Ben Gao, Vincent W. Zheng, Deng Cai, Xiaofei He, Yueting Zhuang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct several experiments on the coauthorship network DBLP and the collaborative development network Github, to show the effectiveness of our apporach for the problem of link prediction in heterogeneous networks. |
| Researcher Affiliation | Academia | Zhou Zhao1, Ben Gao2, Vicent W. Zheng3, Deng Cai2, Xiaofei He2 and Yueting Zhuang1 1College of Computer Science, Zhejiang University 2State Key Lab of CAD&CG, Zhejiang University 3Advanced Digital Sciences Center, Singapore |
| Pseudocode | Yes | Algorithm 1 Path-based Proximity Ranking Metric Learning |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The dataset will be provided later. |
| Dataset Splits | Yes | We sort the network links based on their established timestamps. We use the first 70% links as training set, 10% ones as the validation set and the remaining 20% links as the testing set. |
| Hardware Specification | Yes | We train the proposed method on machines with Linux OS, Intel(R) Core i7-5930K 3.50GHz and two GTX TITAN X graphic cards. |
| Software Dependencies | No | The paper mentions software components like "pre-trained LSTM model," "bidirectional LSTM model," and "Ada Grad" optimizer, but it does not specify version numbers for any libraries, frameworks, or operating system used (e.g., Python version, TensorFlow/PyTorch version). |
| Experiment Setup | Yes | In our approach, there are three essential parameters, which are the dimension of node embedding, the proportion of training data used for model learning and the threshold of path length using both validation datasets. We observe that our method achieves the best performance on Github dataset when the dimension of node embedding is set to 120 and the threshold of path length is set to 7. We also report that the best performance of our method can be achieved on DBLP dataset when the dimension of node embedding is set to 150 and the threshold of path length is set to 9. |