Improving Business Pages Recommendation in Social Network Using Link Prediction Methods
Watare Asaph
School of Science, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, 310023, PR China
Shaowei Sun *
School of Science, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, 310023, PR China
*Author to whom correspondence should be addressed.
Abstract
Recently Social Network has become one of the favorite means for a modern society to perform social interaction and exchange information via the internet. Link prediction is a common problem that has broad application in such social networks, ranging from predicting unobserved interaction to recommending related items. In this paper, we investigate link recommendations over business pages on Facebook Social Network. More specifically, given a company in the
network, we want to recommend potential companies to connect with. We start by introducing existing work in link recommendations and some link prediction models as our baseline. We then talk about the Graph Neural Network model SEAL to make a link recommendations in the network. Our results show that SEAL outperformed the compared baseline model while reaching above 94% Area Under Curve accuracy in link recommendations.
Keywords: Social network, link prediction, facebook pages, graph neural network