HyperEvent: A Strong Baseline for Dynamic Link Prediction via Relative Structural Encoding

arxiv link

Abstract: Learning representations for continuous-time dynamic graphs is critical for dynamic link prediction. While recent methods have become increasingly complex, the field lacks a strong and informative baseline to reliably gauge progress. This paper proposes HyperEvent, a simple approach that captures relative structural patterns in event sequences through an intuitive encoding mechanism. As a straightforward baseline, HyperEvent leverages relative structural encoding to identify meaningful event sequences without complex parameterization. By combining these interpretable features with a lightweight transformer classifier, HyperEvent reframes link prediction as event structure recognition. Despite its simplicity, HyperEvent achieves competitive results across multiple benchmarks, often matching the performance of more complex models. This work demonstrates that effective modeling can be achieved through simple structural encoding, providing a clear reference point for evaluating future advancements.

Datasets

Supports TGB datasets:

• tgbl-wiki-v2
• tgbl-review-v2
• tgbl-coin-v2
• tgbl-comment
• tgbl-flight-v2

Datasets are automatically downloaded when running the code with internet access.

Training & Evaluation

Basic execution use hyperparameters (to reproduce table 1) in config_tgb.yaml:

python train_evaluation_tgb.py --dataset tgbl-wiki

Custom parameters in terminal:

python train_evaluation_tgb.py --dataset tgbl-wiki --num_neighbor 50

After code execution, the following files will be created to automatically record the results.

• logs/: Training logs
• adjtables/: Adjacency tables
• models/: Saved model checkpoints
• results/: Evaluation results (CSV format)

Results

Our experimental environment

• Windows 10
• Python 3.12.4
• PyTorch 2.3.1+cu121
• tgb

Our hardware

• CPU: Intel Core i7-14700KF
• RAM: 64 GB
• GPU: NVIDIA GeForce RTX 4090 (24GB VRAM)