  Urban region profiling is influential for smart cities and sustainable
development. However, extracting fine-grained semantics and generating robust
urban region embeddings from noisy and incomplete urban data is challenging. In
response, we present EUPAC (Enhanced Urban Region Profiling with Adversarial
Contrastive Learning), a novel framework that enhances the robustness of urban
region embeddings through joint optimization of attentive supervised and
adversarial contrastive modules. Specifically, region heterogeneous graphs
containing human mobility data, point of interest information, and geographic
neighborhood details for each region are fed into our model, which generates
region embeddings that preserve intra-region and inter-region dependencies
through graph convolutional networks and multi-head attention. Meanwhile, we
introduce spatially learnable augmentation to generate positive samples that
are semantically similar and spatially close to the anchor, preparing for
subsequent contrastive learning. Furthermore, we propose an adversarial
training method to construct an effective pretext task by generating strong
positive pairs and mining hard negative pairs for the region embeddings.
Finally, we jointly optimize attentive supervised and adversarial contrastive
learning to encourage the model to capture the high-level semantics of region
embeddings while ignoring the noisy and irrelevant details. Extensive
experiments on real-world datasets demonstrate the superiority of our model
over state-of-the-art methods.
