Network Embedding under Partial Monitoring for Evolving Networks
Authors: Yu Han, Jie Tang, Qian Chen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our model from two aspects. The experimental results on real world datasets show that our model outperforms the baseline models by a large margin. |
| Researcher Affiliation | Collaboration | Yu Han1 , Jie Tang1 and Qian Chen2 1Tsinghua University 2Tencent Technology (SZ) Co., Ltd. |
| Pseudocode | Yes | Algorithm 1: Credit Probing Network Embedding |
| Open Source Code | No | The paper does not provide a link or statement about open-sourcing the code for the described methodology. |
| Open Datasets | Yes | AS dataset contains 9 networks, 1 per week between March 31 2001 and May 26 2001 [Leskovec et al., 2005]. |
| Dataset Splits | Yes | We take the first two time stamps for initialization. We use the node vectors learned by our model and the baseline models at each time stamp (except the last time stamp) to predict the new links in the next time interval. We take the new links emerging in the next time interval as positive instances, and randomly sample the equal number of node pairs that never be linked during the next time interval as the negative instances. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set K = 128, λ = 1 and p = 10 for all the models. To be fair, we set p = 20 for all the models. For λ of our model, we set it to be 1. For λ, ζ, and δ of BCGD, we set them in accordance with the paper presenting it. |