Enhanced Urban Region Profiling with Adversarial Self-Supervised
  Learning
Weiliang Chan Qianqian Ren Jinbao Li
Urban region profiling is pivotal for smart cities, but mining fine-grained
semantics from noisy and incomplete urban data remains challenging. In
response, we propose a novel self-supervised graph collaborative filtering
model for urban region embedding called EUPAS. Specifically, region
heterogeneous graphs containing human mobility data, point of interests (POIs)
information, and geographic neighborhood details for each region are fed into
the model, which generates region embeddings that preserve intra-region and
inter-region dependencies through GCNs and multi-head attention. Meanwhile, we
introduce spatial perturbation augmentation to generate positive samples that
are semantically similar and spatially close to the anchor, preparing for
subsequent contrastive learning. Furthermore, adversarial training is employed
to construct an effective pretext task by generating strong positive pairs and
mining hard negative pairs for the region embeddings. Finally, we jointly
optimize supervised and self-supervised learning to encourage the model to
capture the high-level semantics of region embeddings while ignoring the noisy
and unimportant details. Extensive experiments on real-world datasets
demonstrate the superiority of our model over state-of-the-art methods.