# Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

arXiv: https://arxiv.org/abs/1901.11213 · HTML: https://mlsanity.com/paper/1901.11213/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 19 of 19)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs](https://mlsanity.com/paper/2312.03731/) (2023)
- [X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning](https://mlsanity.com/paper/2109.03560/) (2021)
- [ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification](https://mlsanity.com/paper/2204.04618/) (2022)
- [mGNN: Generalizing the Graph Neural Networks to the Multilayer Case](https://mlsanity.com/paper/2109.10119/) (2021)
- [Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering](https://mlsanity.com/paper/2305.06102/) (2023)
- [A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction](https://mlsanity.com/paper/2401.17838/) (2024)
- [Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning](https://mlsanity.com/paper/2403.06482/) (2024)
- [Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems](https://mlsanity.com/paper/2211.08168/) (2022)
- [Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies](https://mlsanity.com/paper/2201.02040/) (2022)
- [Federated Graph-based Networks with Shared Embedding](https://mlsanity.com/paper/2210.01803/) (2022)
- [Multi-view Graph Convolutional Networks with Differentiable Node Selection](https://mlsanity.com/paper/2212.05124/) (2022)
- [Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks](https://mlsanity.com/paper/2406.17073/) (2024)
- [Multi-view graph structure learning using subspace merging on Grassmann manifold](https://mlsanity.com/paper/2204.05258/) (2022)
- [AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation](https://mlsanity.com/paper/2406.02348/) (2024)
- [Self-Supervised State Space Model for Real-Time Traffic Accident Prediction Using eKAN Networks](https://mlsanity.com/paper/2409.05933/) (2024)
- [AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation](https://mlsanity.com/paper/2512.18232/) (2025)
- [Embedding Learning on Multiplex Networks for Link Prediction](https://mlsanity.com/paper/2602.01922/) (2026)

## Abstract (arXiv)

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks.