HetInf: Social Influence Prediction With Heterogeneous Graph Neural Network - Gao, Liqun; Wang, Haiyang; Zhang, Zhouran; Zhuang, Hongwu; Zhou, Bin (Frontiers in Physics, 2022)
With the continuous enrichment of social network applications, such as Tiktok, Weibo, Twitter and other social media 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 effentively modeled and users' potential future behaviors can be predicted, so as to facilitate applications such as recommendation and online advertising. For example, an 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 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 key factor that influence the behavior of users. This paper designs an influence prediction model based on a heterogeneous neural network, HetInf. Specifically, we first construct the heterogeneous social influence network according to the relationship between event nodes and user nodes, then sample the user heterogeneous subgraph for each user, and extract the relevant node features, and finally predict the probability of user behavior through the heterogeneous neural network model. We conduct 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.