  Learning the embeddings for urban regions from human mobility data can reveal
the functionality of regions, and then enables the correlated but distinct
tasks such as crime prediction. Human mobility data contains rich but abundant
information, which yields to the comprehensive region embeddings for cross
domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to
enable the cross domain prediction tasks. First, we integrate the graphs with
spatio-temporal similarity as mobility patterns through a mobility graph fusion
module. Then, in the mobility pattern joint learning module, we design the
multi-level cross-attention mechanism to learn the comprehensive embeddings
from multiple mobility patterns based on intra-pattern and inter-pattern
messages. Finally, we conduct extensive experiments on real-world urban
datasets. Experimental results demonstrate that the proposed MGFN outperforms
the state-of-the-art methods by up to 12.35% improvement.
