Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Time-Aware Random Walk Diffusion to Improve Dynamic Graph Learning
Authors: Jong-whi Lee, Jinhong Jung
AAAI 2023 | Venue PDF | 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]. |