  Mobile user profiling refers to the efforts of extracting users'
characteristics from mobile activities. In order to capture the dynamic varying
of user characteristics for generating effective user profiling, we propose an
imitation-based mobile user profiling framework. Considering the objective of
teaching an autonomous agent to imitate user mobility based on the user's
profile, the user profile is the most accurate when the agent can perfectly
mimic the user behavior patterns. The profiling framework is formulated into a
reinforcement learning task, where an agent is a next-visit planner, an action
is a POI that a user will visit next, and the state of the environment is a
fused representation of a user and spatial entities. An event in which a user
visits a POI will construct a new state, which helps the agent predict users'
mobility more accurately. In the framework, we introduce a spatial Knowledge
Graph (KG) to characterize the semantics of user visits over connected spatial
entities. Additionally, we develop a mutual-updating strategy to quantify the
state that evolves over time. Along these lines, we develop a reinforcement
imitative graph learning framework for mobile user profiling. Finally, we
conduct extensive experiments to demonstrate the superiority of our approach.
