# Multivariate Relations Aggregation Learning in Social Networks

arXiv: https://arxiv.org/abs/2008.03654 · HTML: https://mlsanity.com/paper/2008.03654/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [Higher-order Structure Based Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2406.04690/) (2024)
- [Graph Augmentation Learning](https://mlsanity.com/paper/2203.09020/) (2022)
- [Quantum Graph Learning: Frontiers and Outlook](https://mlsanity.com/paper/2302.00892/) (2023)
- [Network Representation Learning: From Traditional Feature Learning to Deep Learning](https://mlsanity.com/paper/2103.04339/) (2021)
- [Graph Force Learning](https://mlsanity.com/paper/2103.04344/) (2021)
- [Physics-Informed Graph Learning](https://mlsanity.com/paper/2202.10679/) (2022)
- [Collaborative Team Recognition: A Core Plus Extension Structure](https://mlsanity.com/paper/2406.06617/) (2024)
- [PANDORA: Deep graph learning based COVID-19 infection risk level forecasting](https://mlsanity.com/paper/2406.06618/) (2024)

## Abstract (arXiv)

Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle of ternary closures and the trend of group formation, the multivariate relationships in social networks are complex and rich. Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important. Existing graph learning methods are based on the neighborhood information diffusion mechanism, which often leads to partial omission or even lack of multivariate relationship information, and ultimately affects the accuracy and execution efficiency of the task. To address these challenges, this paper proposes the multivariate relationship aggregation learning (MORE) method, which can effectively capture the multivariate relationship information in the network environment. By aggregating node attribute features and structural features, MORE achieves higher accuracy and faster convergence speed. We conducted experiments on one citation network and five social networks. The experimental results show that the MORE model has higher accuracy than the GCN (Graph Convolutional Network) model in node classification tasks, and can significantly reduce time cost.