Multi-view joint graph representation learning for urban region
Moreover, we introduce a joint learning module that boosts the region embedding learning by sharing cross-view information and fuses multi-view embeddings by learning adaptive weights. Finally, we exploit the learned embeddings in the downstream applications of land usage classification and crime prediction in urban areas with real-world data.
PDF Multi-View Joint Graph Representation Learning for Urban Region Embedding
we present an effective multi-view joint learning module. Figure 1: Framework of the proposed multi-view joint representa-tion learning for region embedding. 3.1 Multi-view Correlation Modeling Urban regions are related to each other from multiple aspects. For example, in terms of human mobility activities, like com-
Multi-View Joint Graph Representation Learning for Urban Region ...
Moreover, we introduce a joint learning module that boosts the region embedding learning by sharing cross-view information and fuses multi-view embeddings by learning adaptive weights. Finally, we exploit the learned embeddings in the downstream applications of land usage classification and crime prediction in urban areas with real-world data.
code for paper Multi-View Joint Graph Representation Learning for Urban
Multi-View-Urban-Region-Embedding code for paper Multi-View Joint Graph Representation Learning for Urban Region Embedding (IJCAI 2020) To run the code, cd to model folder:
Region-Wise Attentive Multi-View Representation Learning For Urban
Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions.
Urban Region Profiling via Multi-Graph Representation Learning
Multi-view joint graph representation learning for urban region embedding IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence The increasing amount of urban data enables us to investigate urban dynamics, assist urban planning, and, eventually, make our cities more livable and sustainable.
Region-Wise Attentive Multi-View Representation Learning For Urban
Multi-View Joint Graph Representation Learning for Urban Region Embedding. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan, 4431-4437.
Multi-View Joint Graph Representation Learning for Urban Region Embedding
This paper proposes a multi-view joint learning model to learn comprehensive and representative urban region embeddings and exploits the learned embeddings in the downstream applications of land usage classification and crime prediction in urban areas with real-world data. The increasing amount of urban data enable us to investigate urban dynamics, assist urban planning, and eventually, make ...
Multi-View Joint Graph Representation Learning for Urban Region Embedding
Multi-View Joint Graph Repr esentation Learning for Urban Region Embedding Mingyang Zhang 1 , T ong Li 1 , 3 , Y ong Li 2 and Pan Hui 1 , 3 1 The Hong Kong Univ ersity of Science and Technology
Multi-View Joint Graph Representation Learning for Urban Region ...
The increasing amount of urban data enables us to investigate urban dynamics, assist urban planning, and, eventually, make our cities more livable and sustainable. In this paper, we focus on learning an embedding space from urban data for urban regions. For the first time, we propose a multi-view joint learning model to learn comprehensive and representative urban region embeddings.
COMMENTS
Moreover, we introduce a joint learning module that boosts the region embedding learning by sharing cross-view information and fuses multi-view embeddings by learning adaptive weights. Finally, we exploit the learned embeddings in the downstream applications of land usage classification and crime prediction in urban areas with real-world data.
we present an effective multi-view joint learning module. Figure 1: Framework of the proposed multi-view joint representa-tion learning for region embedding. 3.1 Multi-view Correlation Modeling Urban regions are related to each other from multiple aspects. For example, in terms of human mobility activities, like com-
Moreover, we introduce a joint learning module that boosts the region embedding learning by sharing cross-view information and fuses multi-view embeddings by learning adaptive weights. Finally, we exploit the learned embeddings in the downstream applications of land usage classification and crime prediction in urban areas with real-world data.
Multi-View-Urban-Region-Embedding code for paper Multi-View Joint Graph Representation Learning for Urban Region Embedding (IJCAI 2020) To run the code, cd to model folder:
Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions.
Multi-view joint graph representation learning for urban region embedding IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence The increasing amount of urban data enables us to investigate urban dynamics, assist urban planning, and, eventually, make our cities more livable and sustainable.
Multi-View Joint Graph Representation Learning for Urban Region Embedding. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan, 4431-4437.
This paper proposes a multi-view joint learning model to learn comprehensive and representative urban region embeddings and exploits the learned embeddings in the downstream applications of land usage classification and crime prediction in urban areas with real-world data. The increasing amount of urban data enable us to investigate urban dynamics, assist urban planning, and eventually, make ...
Multi-View Joint Graph Repr esentation Learning for Urban Region Embedding Mingyang Zhang 1 , T ong Li 1 , 3 , Y ong Li 2 and Pan Hui 1 , 3 1 The Hong Kong Univ ersity of Science and Technology
The increasing amount of urban data enables us to investigate urban dynamics, assist urban planning, and, eventually, make our cities more livable and sustainable. In this paper, we focus on learning an embedding space from urban data for urban regions. For the first time, we propose a multi-view joint learning model to learn comprehensive and representative urban region embeddings.