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Semi-supervised User Geolocation via Graph Convolutional Networks

arXiv 1804.08049 ↗First posted 22 Apr 2018cs.CLcited by 27

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Abstract (arXiv)

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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2018.

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Citing papers per year: 2021: 19; 2022: 4; 2023: 0; 2024: 3; 2025: 1.

27 papers.

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  1. 2021On Provable Benefits of Depth in Training Graph Convolutional Networkscited by 58
  2. 2021A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learningcited by 31
  3. 2021Distilling Holistic Knowledge with Graph Neural Networkscited by 26
  4. 2021threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learningcited by 22
  5. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  6. 2021Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systemscited by 21
  7. 2021Privacy-Preserving Graph Convolutional Networks for Text Classificationcited by 19
  8. 2021Unlimited Neighborhood Interaction for Heterogeneous Trajectory Predictioncited by 17
  9. 2021Self-supervised Consensus Representation Learning for Attributed Graphcited by 15
  10. 2022Geographic Adaptation of Pretrained Language Modelscited by 14
  11. 2021NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Datacited by 13
  12. 2021Learning Graph Neural Networks with Positive and Unlabeled Nodescited by 11
15 more
  1. 2024Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Futurecited by 10
  2. 2021Bayesian graph convolutional neural networks via tempered MCMCcited by 9
  3. 2021Relation-aware Heterogeneous Graph for User Profilingcited by 9
  4. 2021DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Abilitycited by 5
  5. 2022Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networkscited by 5
  6. 2021Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networkscited by 3
  7. 2022Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Networkcited by 2
  8. 2021geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classificationcited by 1
  9. 2021Network Embedding via Deep Prediction Modelcited by 1
  10. 2021Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networkscited by 1
  11. 2021Designing weighted and multiplex networks for deep learning user geolocation in Twittercited by 1
  12. 2022Catch Me If You Can: Deceiving Stance Detection and Geotagging Models to Protect Privacy of Individuals on Twittercited by 0
  13. 2024PANDORA: Deep graph learning based COVID-19 infection risk level forecastingcited by 0
  14. 2024Graph Residual based Method for Molecular Property Predictioncited by 0
  15. 2025Forecasting West Nile virus with deep graph encoderscited by 0