The prevalence of region-based urban data has enabled us to explore the correlations of regions for better urban planning and smart-city solutions, in which region embedding plays an important role. However, this is a challenging problem considering the following two factors: different city data for region embedding and the effectiveness for various downstream applications. 
In this paper, we propose a novel framework, named \textbf{HREP} (\textbf{H}eterogeneous \textbf{R}egion \textbf{E}mbedding with \textbf{P}rompt learning), considering both intra-region and inter-region correlations, which includes two major modules, \textbf{H}eterogeneous graph learning for \textbf{R}egion \textbf{E}mbedding (\textbf{HRE}) and prompt learning for different downstream tasks. In the HRE module, we construct a region heterogeneous graph according to three categories of data, capturing inter-region contexts, e.g., human mobility and region geographic neighbors, and intra-region contexts, e.g., POI information. Specially, we design a \textit{relation-aware graph embedding} to learn region embedding and relation embeddings of edge types based on the heterogeneous graph. Then we introduce self-attention to capture the global correlation of regions. Moreover, we develop a \textit{attention-based fusion module} to integrate sharing information among different types of correlations. 
To enable region embedding to be effectively utilized in downstream tasks, we migrate the prompt learning, i.e., continuous prompt
prefix-tuning, which can guide the learning of downstream tasks, leading to better prediction results. Extensive experimental results on real-world datasets show that our model outperforms all state-of-the-art methods dramatically.

Heterogeneous Region Embedding with Prompt Learning

Trajectory similarity computation is a fundamental function in many applications of spatial data analysis. However, most existing methods are designed for trajectories in Euclidean space, ignoring the fact that trajectories in real applications are usually generated on the road networks. In this paper, we study the trajectory similarity computation on road networks with deep learning.
To jointly capture properties of trajectories and road networks, we propose a novel framework \textbf{GRLSTM} (\textbf{G}raph-based \textbf{R}esidual-\textbf{LSTM}), which employs knowledge graph embedding (KGE), graph neural network (GNN), and incorporates the residual network into the multi-layer LSTM (Residual-LSTM). In particular, we study multiple relations of Point-of-Interest (PoI) by constructing a PoI knowledge graph since PoIs may be in the trajectory or in the road network. To integrate multiple relations of each PoI, KGE is introduced to learn PoI embedddings and relation embeddings to build the PoI fusion graph. 
Then, GNN is applied to capture the topology structure information of the PoI fusion graph. Finally, the trajectory embeddings are learned in Residual-LSTM.
To further improve the accuracy and robustness of final trajectory embeddings, we design two new neighbor-based loss functions to optimize our model, i.e., PoI loss function and trajectory-PoI loss function. We conduct a comprehensive evaluation using different real-world trajectory datasets. The experimental results show that GRLSTM significantly outperforms all  state-of-the-art methods.

GRLSTM: Trajectory Similarity Computation with Graph-based Residual LSTM

Posters & Demos IV

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Silin Zhou

3

SHORT BIO

Presentations

Heterogeneous Region Embedding with Prompt Learning

Heterogeneous Region Embedding with Prompt Learning

GRLSTM: Trajectory Similarity Computation with Graph-based Residual LSTM

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