Time-Aware Random Walk Diffusion to Improve Dynamic Graph Learning
Authors: Jong-whi Lee, Jinhong Jung
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Throughout extensive experiments, we demonstrate that TIARA effectively augments a given dynamic graph, and leads to significant improvements in dynamic GNN models for various graph datasets and tasks. |
| Researcher Affiliation | Academia | Jong-whi Lee and Jinhong Jung* Department of Computer Science and Artificial Intelligence, Jeonbuk National University, South Korea |
| Pseudocode | Yes | Algorithm 1: TIARA at time t |
| Open Source Code | Yes | The code of TIARA and the datasets are publicly available at https://github.com/dev-jwel/Tia Ra. |
| Open Datasets | Yes | Table 1 summarizes 7 public datasets used in this work. Bitcoin Alpha is a social network between bitcoin users (Kumar et al. 2016, 2018b). Wiki Elec is a voting network for Wikipedia adminship elections (Leskovec, Huttenlocher, and Kleinberg 2010). Reddit Body is a hyperlink network of connections between two subreddits (Kumar et al. 2018a). For node classification, we use the following datasets evaluated in (Xu et al. 2019). Brain is a network of brain tissues where edges indicate their connectivities. DBLP-3 and DBLP-5 are co-authorship networks extracted from DBLP. Reddit is a post network where two posts were connected if they contain similar keywords. The code of TIARA and the datasets are publicly available at https://github.com/dev-jwel/Tia Ra. |
| Dataset Splits | Yes | For each dataset, we tune the hyperparameters of all models on the original graph (marked as NONE) and augmented graphs separately through a combination of grid and random search on a validation set, and report test accuracy at the best validation epoch. ... As a standard setting (Pareja et al. 2020), we follow a chronological split with ratios of training (70%), validation (10%), and test (20%) sets. |
| Hardware Specification | Yes | All experiments were done at workstations with Intel Xeon 4215R and RTX 3090. |
| Software Dependencies | No | We use Py Torch and DGL (Wang et al. 2019) to implement all methods. Specific version numbers for PyTorch or DGL are not provided. |
| Experiment Setup | Yes | For TIARA, we fix K to 100, search for ϵ in [0.0001, 0.01], and tune α and β in (0, 1) s.t. 0 < α + β < 1. We use the Adam optimizer with weight decay 10 4, and the learning rate is tuned in [0.01, 0.05] with decay factor 0.999. The dropout ratio is searched in [0, 0.5]. |