Semi-supervised User Geolocation via Graph Convolutional Networks
By Afshin Rahimi, Trevor Cohn, Timothy Baldwin. 2018
172 citations recorded by the source corpus.
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 Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our model achieves or is competitive with the state-of-the-art over three benchmark geolocation datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that highway network gates are essential for controlling the amount of useful neighbourhood expansion in GCN.
Subfields: Epidemiology, Geography, Planning and Development, Transportation
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Citations in the corpus
- Simplifying Graph Convolutional Networks (2019, methodology): “Rahimi et al. (2018) apply GCNs with highway connections on this task and achieve close to state-of-the-art results.”
- DeepGCNs: Can GCNs Go As Deep As CNNs? (2019, unknown): “[31] developed a Highway GCN for user geo-location in social media graphs, where they add “highway” gates between layers to facilitate gradient flow.”
- The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “Incorporating user networks into geolocation substantially improves performance (Rahimi et al., 2018; Fornaciari and Hovy, 2019) and Dinan et al.”
- DeeperGCN: All You Need to Train Deeper GCNs (2020, background): “Several works attempt different ways of training deeper GCNs [Hamilton et al., 2017, Armeni et al., 2017, Rahimi et al., 2018, Xu et al., 2018].”
- Graph Neural Networks: A Review of Methods and Applications (2018, methodology): “[56] proposed a Highway GCN which uses layer-wise gates similar to highway networks [57].”
- Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management (2020, background): “is represented by a graph neural network (GNN) [9]–[11].”
- Explainability Techniques for Graph Convolutional Networks (2019, background): “It was further extended, by gating (Li et al., 2016), convolutions in spectral (Bruna et al., 2014) and spatial (Kipf & Welling, 2017) domains, skip connections (Rahimi et al., 2018) and recently attention (Velickovic et al., 2018).”