DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction

• URL: http://arxiv.org/abs/2208.06794v1
• Date: Sun, 14 Aug 2022 06:51:54 GMT
• Title: DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
• Authors: Yinfeng Li, Chen Gao, Quanming Yao, Tong Li, Depeng Jin, Yong Li
• Abstract summary: We propose a hypergraph network model called DisenHCN to bridge the gaps in existing solutions. In particular, we first unify fine-grained user similarity and the complex matching between user preferences andtemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph.
• Score: 53.76601630407521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Existing solutions based on tensor decomposition or graph embedding suffer from the following two major limitations: 1) ignoring the fine-grained similarities of user preferences; 2) user's modeling is entangled. In this work, we propose a hypergraph neural network model called DisenHCN to bridge the above gaps. In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph, capturing high-order relations from different aspects and disentangles the impact of each aspect for final prediction. Extensive experiments show that our DisenHCN outperforms the state-of-the-art methods by 14.23% to 18.10% on four real-world datasets. Further studies also convincingly verify the rationality of each component in our DisenHCN.

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