Introduction

This repository provides the codes of the paper "Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction" and "AoI-Based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching".

Note that this is a research project and by definition is unstable. Please write to us if you find something not correct or strange. We are sharing the codes under the condition that reproducing full or part of codes must cite our papers.

Paper link:

AoI-Based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching

emantics-enhanced Temporal Graph Networks for Content Popularity Prediction

Feature

• An attention aggregator for the raw message processing.

• An AoI-based message filter with the attention aggregator.

• Different semantic aggregators for the representation learning.

Running the experiments

Requirements

pip install -r requirements.txt

Dataset and Preprocessing

The public data

Download the wikipedia and reddit datasets from here and netflix dataset from here.

You can also use the data we saved in the folder ./data.

Preprocess the data

python utils/netflix_process.py --bipartite --coder BERT

Model Training

Training AoI-Based TGN:

# AoI Attention:
python train_self_supervised.py train_self_supervised.py --n_runs 5 --n_epoch 50 --aggregator attn --prefix TGN-A --data wikipedia --n_neighbor 6 --use_memory --use_age --bs 200

Training STGN:

# M2-STGN:
python train_self_supervised.py train_self_supervised.py --n_runs 5 --n_epoch 50 --aggregator attn --prefix STGN-A --data netflix --n_neighbor 6 --use_memory --use_age --bs 200 --Sem --mix Attn

Cite us

@ARTICLE{9978680,
  author={Zhu, Jianhang and Li, Rongpeng and Ding, Guoru and Wang, Chan and Wu, Jianjun and Zhao, Zhifeng and Zhang, Honggang},
  journal={IEEE Transactions on Cognitive Communications and Networking}, 
  title={AoI-Based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching}, 
  year={2023},
  volume={9},
  number={2},
  pages={345-358},
  doi={10.1109/TCCN.2022.3227920}}

@ARTICLE{10380461,
  author={Zhu, Jianhang and Li, Rongpeng and Chen, Xianfu and Mao, Shiwen and Wu, Jianjun and Zhao, Zhifeng},
  journal={IEEE Transactions on Mobile Computing}, 
  title={Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction}, 
  year={2024},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TMC.2023.3349315}}

Acknowledgements

This repository is based on modifications and extensions of TGN. We express our gratitude for the original contributions.