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

## TL;DR

This paper introduces Multi-GCN, a graph convolutional network designed for multi-view networks, demonstrating superior performance in global poverty prediction and other multi-view learning tasks across various datasets.

## Contribution

The paper presents a novel Multi-GCN model that effectively captures multi-view relations in graphs, improving semi-supervised learning in poverty research and beyond.

## Key findings

- Outperforms state-of-the-art algorithms on poverty prediction tasks.
- Achieves better results on multi-view node labeling in citation networks.
- Effective across datasets from multiple developing countries.

## Abstract

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.