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Semi-supervised User Geolocation via Graph Convolutional Networks

by   Afshin Rahimi, et al.

Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Con- volutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two base- lines we propose, and show that our model achieves or is competitive with the state- of-the-art over three benchmark geoloca- tion datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that high- way network gates are essential for control- ling the amount of useful neighbourhood expansion in GCN.


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Semi-supervised User Geolocation via Graph Convolutional Networks

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