Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
Authors: Lili Wang, Chongyang Gao, Chenghan Huang, Ruibo Liu, Weicheng Ma, Soroush Vosoughi10147-10155
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct thorough experiments for the tasks of network reconstruction and link prediction on two public datasets, showing that our model outperforms a variety of well-known baselines across all tasks. |
| Researcher Affiliation | Collaboration | Lili Wang,1 Chongyang Gao, 1 Chenghan Huang, 2 Ruibo Liu, 1 Weicheng Ma, 1 Soroush Vosoughi 1 1 Department of Computer Science, Dartmouth College 2 Millennium Management LLC lili.wang.gr@dartmouth.edu, soroush.vosoughi@dartmouth.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The code and data for this paper will be made available upon request. |
| Open Datasets | Yes | DBLP: This is a subset network of DBLP, which contains three node types: 14,475 authors (A), 14,376 papers (P), and 20 venues (V). The network also contains the following edge types: 41,794 paper authorship relations (P-A) and 14,376 publish relations (P-V). Movie Lens: This is a subset network of Movie Lens, which contains three node types: 11,718 actors (A), 9,160 movies (M), and 3,510 directors (D). The network also contains the following edge types: 64,051 act in relations (M-A) and 9,160 direct relations (M-D). |
| Dataset Splits | No | The paper describes the creation of the test set by randomly removing 20% of edges, but does not explicitly mention a separate validation set or its split details. |
| Hardware Specification | Yes | All the experiments are run on an Amazon AWS p2.8xlarge instance running a Linux OS with 488GB of RAM, and the random seeds are set to 0 at the beginning. |
| Software Dependencies | No | The paper mentions 'Linux OS' but does not provide specific version numbers for software dependencies or libraries used for the implementation (e.g., PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | For our method, we use the following parameters: We do 10 random walks from each node in the training set with the length of 80, and use a sliding window of size 5 to generate positive samples. For the hyperboloid embedding learning, we generate 20 negative samples for each positive one, and use the learning rate of 0.3 and a batch size of 512 to train 5 epochs. |