DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models

This repository is built for our TMLR paper DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models

Dynamic Graph Learning Models

The directory contains seven benchmark dataset and our work DyGMamba, which is an efficient continuous-time dynamic graph(CTDG) representation learning model that can capture long-term temporal dependencies. It also contains previous CTDG learning methods, including JODIE, DyRep, TGAT, TGN, CAWN, TCL, GrapnMixer and DyGFormer.

Evaluation Tasks

The code supports dynamic link prediction under both transductive and inductive settings with three (i.e., random, historical, and inductive) negative sampling strategies.

Environments

PyTorch, numpy, pandas, tqdm, tabulate, and mamba-ssm

To install the packages, run

pip install -r requirements.txt

Executing Scripts

We test our model using seven datasets: wikipedia, reddit, mooc, lastfm, enron, SocialEvo and uci. They can be downloaded. To run the training or evaluation, please download the datasets and put the unzipped file in processed_data folder.

For example, the directory of uci dataset should be structured as follows:

project-root/
└── processed_data/
    └── uci/
        ├── ml_uci.csv
        ├── ml_uci.npy
        └── ml_uci_node.npy

Scripts for Dynamic Link Prediction

Dynamic link prediction could be performed on all the seven datasets. If you want to load the best model configurations determined by the grid search, please set the load_best_configs argument to True.

Model Training

• Example of training DyGMamba on Wikipedia dataset:

python train_link_prediction.py --dataset_name wikipedia --model_name DyGMamba --patch_size 2 --max_input_sequence_length 64 --num_runs 5 --gpu 0

• If you want to use the best model configurations to train DyGMamba on Wikipedia dataset, run

python train_link_prediction.py --dataset_name wikipedia --model_name DyGMamba --load_best_configs --num_runs 5 --gpu 0

Model Evaluation

Three (i.e., random, historical, and inductive) negative sampling strategies can be used for model evaluation.

• Example of evaluating DyGMamba with random negative sampling strategy on Wikipedia dataset:

python evaluate_link_prediction.py --dataset_name wikipedia --model_name DyGMamba --patch_size 2 --max_input_sequence_length 64 --negative_sample_strategy random --num_runs 5 --gpu 0

• If you want to use the best model configurations to evaluate DyGMamba with random negative sampling strategy on Wikipedia dataset, run

python evaluate_link_prediction.py --dataset_name wikipedia --model_name DyGMamba --negative_sample_strategy random --load_best_configs --num_runs 5 --gpu 0