Reconciling Competing Sampling Strategies of Network Embedding
Authors: Yuchen Yan, Baoyu Jing, Lihui Liu, Ruijie Wang, Jinning Li, Tarek Abdelzaher, Hanghang Tong
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the effectiveness of the proposed algorithm (SENSEI) for solving link prediction and node recommendation simultaneously in plain networks. ...The results of link prediction on all 4 plain networks are presented in Table 1. |
| Researcher Affiliation | Academia | Yuchen Yan, Baoyu Jing, Lihui Liu, Ruijie Wang, Jinning Li, Tarek Abdelzaher, Hanghang Tong University of Illinois at Urbana Champaign, IL, USA {yucheny5, baoyuj2, lihuil2, ruijiew2, jinning4, zaher, htong}@illinois.edu |
| Pseudocode | Yes | In this section, we give the detailed algorithm of SENSEI in Algorithm 1. |
| Open Source Code | Yes | The simplified code of SENSEI is on: https://github.com/yucheny5/SENSEI. |
| Open Datasets | Yes | Datasets. We use 4 public real-world datasets to evaluate the proposed SENSEI model: C.ele [51], Cora [39], Citeseer [39], NS [31]. |
| Dataset Splits | Yes | For link prediction and node recommendation in plain networks, we randomly split edges in every dataset into 70/10/20% for training, validation, and test. |
| Hardware Specification | Yes | All experiments are run on a Tesla-V100 GPU. |
| Software Dependencies | No | The paper mentions the hardware used but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For SENSEI on 4 datasets: {C.ele, Cora, Citeseer, NS}, we set the threshold τ as {0.008, 0.01, 0.005, 0.05}, the number of epochs in Step 1 as {40, 100, 20, 20}, the number of epochs in Step 2 as {40, 100, 50, 20}, the learning rate in Step 1 as {0.02, 0.1, 0.1, 0.2}, the learning rate in Step 2 as {0.01, 0.01, 0.005, 0.1}, the positive margin γ as {0.05, 0.0001, 0.0001, 0.1} and the negative sample number k as 40 on all 4 datasets. |