Multivariate Relations Aggregation Learning in Social Networks

TL;DR

This paper introduces MORE, a novel graph learning method that effectively captures multivariate relationship information in social networks, improving accuracy and efficiency over existing neighborhood-based approaches.

Contribution

The paper proposes the MORE method, which aggregates node attributes and structural features to better utilize multivariate relationships in social network graph learning.

Findings

MORE outperforms GCN in node classification accuracy

It significantly reduces computational time

Demonstrated on multiple social networks and a citation network

Abstract

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.