Potential behavior prediction involves understanding the latent human behavior of specific groups, and can 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 real-world 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.

潜在的行为预测包括了解特定人群的潜在人类行为，并可以帮助组织制定战略决策。信息技术的进步使得获取有关人类行为的越来越多的数据成为可能。在本文中，我们将在现实场景中获得的行为数据作为一个信息网络进行检查，该信息网络由与各种属性相关联的两种类型的对象（人类和动作）以及三种类型的关系（人类-人类，人类行为和行为-行动），我们称之为异构行为网络（HBN）。为了充分利用HBN的丰富性和异构性，我们提出了一种新颖的网络嵌入方法，即具有人类动作属性感知能力的异构网络嵌入方法（⁴HNE），它共同考虑了结构邻近性，属性相似性和异质性融合。在两个真实世界的数据集上进行的实验表明，该方法在各种异构信息网络挖掘任务中的潜在行为预测方面优于其他类似方法。