# Semi-supervised User Geolocation via Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1804.08049 · HTML: https://mlsanity.com/paper/1804.08049/

## Key facts

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

## Built on by (most cited first, 27 of 27)

- [On Provable Benefits of Depth in Training Graph Convolutional Networks](https://mlsanity.com/paper/2110.15174/) (2021)
- [A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning](https://mlsanity.com/paper/2103.15059/) (2021)
- [Distilling Holistic Knowledge with Graph Neural Networks](https://mlsanity.com/paper/2108.05507/) (2021)
- [threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning](https://mlsanity.com/paper/2111.04333/) (2021)
- [Hybrid Quantum-Classical Graph Convolutional Network](https://mlsanity.com/paper/2101.06189/) (2021)
- [Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems](https://mlsanity.com/paper/2102.06349/) (2021)
- [Privacy-Preserving Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2102.09604/) (2021)
- [Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2108.00238/) (2021)
- [Self-supervised Consensus Representation Learning for Attributed Graph](https://mlsanity.com/paper/2108.04822/) (2021)
- [Geographic Adaptation of Pretrained Language Models](https://mlsanity.com/paper/2203.08565/) (2022)
- [NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/2106.11865/) (2021)
- [Learning Graph Neural Networks with Positive and Unlabeled Nodes](https://mlsanity.com/paper/2103.04683/) (2021)
- [Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future](https://mlsanity.com/paper/2403.14659/) (2024)
- [Bayesian graph convolutional neural networks via tempered MCMC](https://mlsanity.com/paper/2104.08438/) (2021)
- [Relation-aware Heterogeneous Graph for User Profiling](https://mlsanity.com/paper/2110.07181/) (2021)
- [DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability](https://mlsanity.com/paper/2111.10447/) (2021)
- [Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks](https://mlsanity.com/paper/2206.00583/) (2022)
- [Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks](https://mlsanity.com/paper/2105.08330/) (2021)
- [Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network](https://mlsanity.com/paper/2207.07503/) (2022)
- [geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification](https://mlsanity.com/paper/2101.11424/) (2021)
- [Network Embedding via Deep Prediction Model](https://mlsanity.com/paper/2104.13323/) (2021)
- [Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks](https://mlsanity.com/paper/2106.13061/) (2021)
- [Designing weighted and multiplex networks for deep learning user geolocation in Twitter](https://mlsanity.com/paper/2112.06999/) (2021)
- [Catch Me If You Can: Deceiving Stance Detection and Geotagging Models to Protect Privacy of Individuals on Twitter](https://mlsanity.com/paper/2207.11500/) (2022)
- [PANDORA: Deep graph learning based COVID-19 infection risk level forecasting](https://mlsanity.com/paper/2406.06618/) (2024)
- [Graph Residual based Method for Molecular Property Prediction](https://mlsanity.com/paper/2408.03342/) (2024)
- [Forecasting West Nile virus with deep graph encoders](https://mlsanity.com/paper/2509.22657/) (2025)

## 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.