Weather shocks, maize yields and adaptation in rural China

Based on panel household data collected between 2004 and 2010, we assess the impact of weather shocks on maize yields in the two main producing regions in China, the Northern spring maize zone and the Yellow-Huai Valley summer maize zone. Temperature, drought, wet conditions, and precipitations have detrimental effects on maize yields in the two maize zones. Nonetheless, the magnitude of those effects appears to be low compared to other parts of the world. Adaptation seems to be key in the region where the largest impact is estimated. On the contrary, the lower impact found in the other region, the Yellow-Huai Valley summer maize zone, is low but likely to intensify

Is Homophily a Necessity for Graph Neural Networks?

Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node classification, GNNs are widely believed to work well due to the homophily assumption ("like attracts like"), and fail to generalize to heterophilous graphs where dissimilar nodes connect. Recent works design new architectures to overcome such heterophily-related limitations, citing poor baseline performance and new architecture improvements on a few heterophilous graph benchmark datasets as evidence for this notion. In our experiments, we empirically find that standard graph convolutional networks (GCNs) can actually achieve better performance than such carefully designed methods on some commonly used heterophilous graphs. This motivates us to reconsider whether homophily is truly necessary for good GNN performance. We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions. Our work carefully characterizes these conditions, and provides supporting theoretical understanding and empirical observations. Finally, we examine existing heterophilous graphs benchmarks and reconcile how the GCN (under)performs on them based on this understanding