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

arXiv: https://arxiv.org/abs/2208.08606 · HTML: https://mlsanity.com/paper/2208.08606/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 18 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey](https://mlsanity.com/paper/2005.07496/) (2020)
- [Dynamic Graph Representation Learning via Self-Attention Networks](https://mlsanity.com/paper/1812.09430/) (2018)
- [Multi-Component Graph Convolutional Collaborative Filtering](https://mlsanity.com/paper/1911.10699/) (2019)
- [A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks](https://mlsanity.com/paper/1712.07084/) (2017)
- [The Role of Caching in Future Communication Systems and Networks](https://mlsanity.com/paper/1805.11721/) (2018)
- [Content Popularity Prediction Towards Location-Aware Mobile Edge Caching](https://mlsanity.com/paper/1809.00232/) (2018)
- [A Feature-Based Bayesian Method for Content Popularity Prediction in Edge-Caching Networks](https://mlsanity.com/paper/1905.09824/) (2019)

## Built on by (most cited first, 2 of 2)

- [NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services](https://mlsanity.com/paper/2307.06148/) (2023)
- [Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction](https://mlsanity.com/paper/2301.12355/) (2023)

## Abstract (arXiv)

Along with the fast development of network technology and the rapid growth of network equipment, the data throughput is sharply increasing. To handle the problem of backhaul bottleneck in cellular network and satisfy people's requirements about latency, the network architecture like information-centric network (ICN) intends to proactively keep limited popular content at the edge of network based on predicted results. Meanwhile, the interactions between the content (e.g., deep neural network models, Wikipedia-alike knowledge base) and users could be regarded as a dynamic bipartite graph. In this paper, to maximize the cache hit rate, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph. Furthermore, in order to have deeper insights into the dynamics within the evolving graph, we propose an age of information (AoI) based attention mechanism to extract valuable historical information while avoiding the problem of message staleness. Combining this aforementioned prediction model, we also develop a cache selection algorithm to make caching decisions in accordance with the prediction results. Extensive results demonstrate that our model can obtain a higher prediction accuracy than other state-of-the-art schemes in two real-world datasets. The results of hit rate further verify the superiority of the caching policy based on our proposed model over other traditional ways.