Recent Link Classification on Temporal Graphs Using Graph Profiler
Authors: Muberra Ozmen, Thomas Markovich
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Secondly, we evaluate the performance of state-of-the-art methods with a statistically meaningful metric Matthews Correlation Coefficient, which is more robust to imbalanced datasets, in addition to the commonly used average precision and area under the curve. We propose several design principles for tailoring models to specific requirements of the task and the dataset including modifications on message aggregation schema, readout layer and time encoding strategy which obtain significant improvement on benchmark datasets. Finally, we propose an architecture that we call Graph Profiler, which is capable of encoding previous events class information on source and destination nodes. The experiments show that our proposed model achieves an improved Matthews Correlation Coefficient on most cases under interest. |
| Researcher Affiliation | Industry | Muberra Ozmen EMAIL Cash App Montreal, QC, Canada Thomas Markovich EMAIL Cash App Cambridge, MA, USA |
| Pseudocode | No | The paper describes the architecture and its components in Section 4 with numbered steps for the overall flow, but it does not provide a clearly labeled pseudocode block or algorithm. |
| Open Source Code | No | The paper mentions using open-source implementations for third-party tools: "We implemented TGN using the layers that are publicly available in Py Torch Geometric, Graph Mixer 3 and TGAT 4 were implemented using the authors opensource implementation provided on their github repository." However, it does not state that the code for their own proposed methodology (Graph Profiler) or their modifications is open-source or provide a link. |
| Open Datasets | Yes | We evaluated our methods on four benchmark datasets that have previously been used by the TGL community Yelp CHI (Dou et al., 2020), Wikipedia, Mooc, and Reddit (Kumar et al., 2019). ... In addition to adapting benchmark TGL datasets to RLC task, we process two tabular datasets that are not conventionally investigated in TGL setting; Epic Games 1 and Open Sea (La Cava et al., 2023a;b; Costa et al., 2023) 2. 1https://www.kaggle.com/datasets/mexwell/epic-games-store-dataset. 2https://huggingface.co/datasets/MLNTeam-Unical/NFT-70M_transactions. |
| Dataset Splits | Yes | In our experiments, data is divided into training (70%), validation (10%) and testing (20%) sets chronologically. |
| Hardware Specification | Yes | All computations were run on an Nvidia DGX A100 machine with 128 AMD Rome 7742 cores and 8 Nvidia A100 GPUs. |
| Software Dependencies | Yes | All models were implemented using Py Torch Geometric 2.3.1 (Fey & Lenssen, 2019) and Py Torch 1.13 (Paszke et al., 2019). |
| Experiment Setup | No | The paper mentions performing hyperparameter optimization using Optuna and discussing general design principles like time encoding strategies, message aggregators, and readout layers. However, it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) for the final models or a dedicated section detailing the configuration setup with concrete values. |