Player Behavior Profiling through Provenance Graphs and Representation Learning.

Arguably, player behavior profiling is one of the most relevant tasks of Game Analytics. However, to fulfill the needs of this task, gameplay data should be handled so that the player behavior can be profiled and even understood. Usually, gameplay data is stored as raw log-like files, from which gameplay metrics are computed. However, gameplay metrics have been commonly used as input to classify player behavior with two drawbacks: (1) gameplay metrics are mostly handcrafted and (2) they might not be adequate for fine-grain analysis as they are just computed after key events, such as stage or game completion. In this paper, we present a novel approach for player profiling based on provenance graphs, an alternative to log-like files that model causal relationships between entities in game. Our approach leverages recent advances in deep learning over graph representation of player states and its neighboring contexts, requiring no handcrafted features. We perform clustering on learned nodes representations to profile at a fine-grain the player behavior in provenance data collected from a multiplayer battle game and assess the obtained profiles through statistical analysis and data visualization.