Multivariate Relations Aggregation Learning in Social Networks

08/09/2020
by   Jin Xu, et al.
0

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.

READ FULL TEXT

page 5

page 6

11/16/2021

SStaGCN: Simplified stacking based graph convolutional networks

Graph convolutional network (GCN) is a powerful model studied broadly in...
02/13/2019

Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks

Graph convolutional networks (GCNs) have been successfully applied in no...
02/11/2020

LoCEC: Local Community-based Edge Classification in Large Online Social Networks

Relationships in online social networks often imply social connections i...
04/04/2019

DAGCN: Dual Attention Graph Convolutional Networks

Graph convolutional networks (GCNs) have recently become one of the most...
11/16/2021

Analysis of 5G academic Network based on graph representation learning method

With the rapid development of 5th Generation Mobile Communication Techno...
09/29/2020

A local geometry of hyperedges in hypergraphs, and its applications to social networks

In many real world datasets arising from social networks, there are hidd...
04/25/2022

A new preferential model with homophily for recommender systems

"Rich-get-richer" and "homophily" are two important phenomena in evolvin...