Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
This work addresses the multi-view nature of social networks for poverty research and related domains, offering a novel method that improves prediction accuracy in real-world applications.
The paper tackles the problem of graph learning on multi-view social networks, which are common in applications like global poverty estimation, by developing a graph convolutional network that outperforms state-of-the-art semi-supervised learning algorithms on prediction tasks using mobile phone datasets from three developing countries, achieving improved performance in node labeling tasks.
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.