MGNN: mutualistic graph neural network for joint friend and item recommendation

Yang Xiao, Lina Yao, Qingqi Pei, Xianzhi Wang, Jian Yang, Quan Z. Sheng

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
8 Downloads (Pure)


Many social studies and practical cases suggest that people's consumption behaviors and social behaviors are not isolated but interrelated in social network services. However, most existing research either predicts users' consumption preferences or recommends friends to users without dealing with them simultaneously. We propose a holistic approach to predict users' preferences on friends and items jointly and thereby make better recommendations. To this end, we design a graph neural network that incorporates a mutualistic mechanism to model the mutual reinforcement relationship between users' consumption behaviors and social behaviors. Our experiments on the two-real world datasets demonstrate the effectiveness of our approach in both social recommendation and link prediction.

Original languageEnglish
Pages (from-to)7-17
Number of pages11
JournalIEEE Intelligent Systems
Issue number5
Early online date24 Apr 2020
Publication statusPublished - Sep 2020

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Graph Neural Networks
  • Joint Recommendation
  • Mutualistic Model
  • Social Networks


Dive into the research topics of 'MGNN: mutualistic graph neural network for joint friend and item recommendation'. Together they form a unique fingerprint.

Cite this