Distance-Aware DAG Embedding for Proximity Search on Heterogeneous Graphs
Authors: Zemin Liu, Vincent Zheng, Zhou Zhao, Fanwei Zhu, Kevin Chang, Minghui Wu, Jing Ying
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate D2AGE on three benchmark data sets with six semantic relations, and we show that D2AGE outperforms the state-of-the-art baselines. We release the code on https://github.com/shuai OKshuai. |
| Researcher Affiliation | Academia | 1 Zhejiang University, China; 2 Advanced Digital Sciences Center, Singapore; 3 Zhejiang University City College, China; 4 University of Illinois at Urbana-Champaign, USA |
| Pseudocode | Yes | Algorithm 1 Distance-aware DAG Generation; Algorithm 2 Distance-aware DAG Embedding |
| Open Source Code | Yes | We release the code on https://github.com/shuai OKshuai. |
| Open Datasets | Yes | The Linkedin dataset (Li, Wang, and Chang 2014) contains two symmetric relations: schoolmate and colleague; the Facebook dataset (Li, Wang, and Chang 2014) contains two symmetric relations: classmate and family; and the DBLP dataset (Wang et al. 2010) contains two asymmetric relations: advisor and advisee. |
| Dataset Splits | Yes | We randomly sample 20% of queries as the training set and 80% as the test set. We repeat this procedure for five times and report the average performance. |
| Hardware Specification | Yes | We run our experiments on Linux machines with eight 2.27GHz Intel Xeon(R) CPUs and 32GB memory. |
| Software Dependencies | Yes | We use java-1.8 and Theano (Team 2016) for development. |
| Experiment Setup | Yes | In the experiments, we set λ = 0.0001, then we tune parameters d, α, β, μ for different datasets. |