Learning embeddings of a heterogeneous behavior network for potential behavior prediction

Abstract

Potential behavior prediction involves understanding the latent human behavior of specific groups, andcan assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a4HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

Keywords

Network embedding / Representation learning / Human behavior / Social networks / Heterogeneous information network / Attribute

RIGHTS & PERMISSIONS

2020 Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature