HetInf: Social Influence Prediction With Heterogeneous Graph Neural Network

Liqun Gao,Hongwu Zhuang,Haiyang Wang,Bin Zhou,Zhouran Zhang

doi:10.3389/fphy.2021.787185

Liqun Gao, Hongwu Zhuang + Show 3 more

Open Access

https://doi.org/10.3389/fphy.2021.787185

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Abstract

With the continuous enrichment of social network applications, such as TikTok, Weibo, Twitter, and others, social media have become an indispensable part of our lives. Web users can participate in their favorite events or pay attention to people they like. The “heterogeneous” influence between events and users can be effectively modeled, and users’ potential future behaviors can be predicted, so as to facilitate applications such as recommendations and online advertising. For example, a user’s favorite live streaming host (user) recommends certain products (event), can we predict whether the user will buy these products in the future? The majority of studies are based on a homogeneous graph neural network to model the influence between users. However, these studies ignore the impact of events on users in reality. For instance, when users purchase commodities through live streaming channels, in addition to the factors of the host, the commodity is also a key factor that influences the behavior of users. This study designs an influence prediction model based on a heterogeneous neural network HetInf. Specifically, we first constructed the heterogeneous social influence network according to the relationship between event nodes and user nodes, then sampled the user heterogeneous subgraph for each user, extracted the relevant node features, and finally predicted the probability of user behavior through the heterogeneous neural network model. We conducted comprehensive experiments on two large social network datasets. Furthermore, the experimental results show that HetInf is significantly superior to the previous homogeneous neural network methods.

Highlights

Nowadays, social networks are everywhere around us in our daily lives
We have identified a huge development of the graph neural network in deep learning technology [27,49], and the state-of-the-art model GAT [19], which represents the method of depth learning-based graphical representation as the graph neural network (GNN), the main idea is as follows: the first step is to calculate the feature representation of neighbor nodes, and the second step is to aggregate neighbors through message passing mechanism to obtain the feature representation of nodes [50]
We studied the problem of influence prediction based on a heterogeneous neural network, introduced a novel model HetInf that combines three neural network modules

Summary

Introduction

Social networks are everywhere around us in our daily lives. Social influence occurs when we get information from social networks, which means that network events (such as network news, trending topics, publishing papers, or other events) or network users we are interested in constantly influence us through social media, and both of them can induce us to engage in social action (including retweet, comment, like, publish, and purchase). Live commerce is very popular nowadays, and we will choose our favorite live streaming host to buy necessary commodities From another perspective, both the live streaming host (user) and the commodities (event) have a substantial impact on the target user’s behavior. How to model the influence relationship to predict the behavior of network users on events is one of the key computational problems in user-level social influence prediction. This problem is applied to many fields, including but not limited to election [2], network marketing [3,4], recommendation [5], rumor detection [6], and information dissemination[7,8]

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