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.

本文提出了一种名为多图融合网络（MGFN）的模型，通过人类移动数据的地理位置相似性融合模块，将人类移动数据的多种模式嵌入到该模型中，这有助于进行交叉领域的预测任务，如犯罪预测，并在实际城市数据集上进行了广泛的实验，结果表明，MGFN 模型优于现有技术方法，最大提高了 12.35％。