Game Analytics comprises a set of techniques to analyze both the game quality and player behavior. To succeed in Game Analytics, it is essential to identify what is happening in a game (an effect) and track its causes. Thus, game provenance graph tools have been proposed to capture cause-and-effect relationships occurring in a gameplay session to assist the game design process. However, since game provenance data capture is guided by a set of strict predefined rules established by the game developers, the detection of long-range cause-and-effect relationships may demand huge coding efforts. In this paper, we contribute with a framework named PingUMiL that leverages the recently proposed graph embeddings to represent game provenance graphs in a latent space. The embeddings learned from the data pose as the features of a machine learning task tailored towards detecting long-range cause-and-effect relationships. We evaluate the generalization capacity of PingUMiL when learning from similar games and compare its performance to classical machine learning methods. The experiments conducted on two racing games show that (1) PingUMiL outperforms classical machine learning methods and (2) representation learning can be used to detect long-range cause-and-effect relationships in only partially observed game data provenance graphs.

游戏分析包括一套技术，可同时分析游戏质量和玩家行为。为了在Game Analytics中取得​​成功，必须识别游戏中发生的事情（一种效果）并跟踪其原因。因此，已经提出了游戏出处图工具来捕获在游戏过程中发生的因果关系，以辅助游戏设计过程。但是，由于游戏出处数据捕获是由游戏开发人员建立的一组严格的预定义规则指导的，因此，长距离因果关系的检测可能需要大量的编码工作。在本文中，我们提供了一个名为PingUMiL的框架，该框架利用了最近提出的图形嵌入来表示潜在空间中的游戏出处图。从数据中学习到的嵌入构成了机器学习任务的特征，旨在检测远程因果关系。我们从类似游戏中学习时评估PingUMiL的泛化能力，并将其性能与经典机器学习方法进行比较。在两个赛车游戏上进行的实验表明，（1）PingUMiL优于传统的机器学习方法，（2）表示学习可用于仅在部分观察到的游戏数据出处图中检测远距离因果关系。