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

Construction health and safety: A topic landscape study

We aim to draw in-depth insights into the current literature in construction health and safety and provide perspectives for future research efforts. The existing literature on construction health and safety is not only diverse and rich in sight, but also complex and fragmented in structure. It is essential for the construction industry and research community to understand the overall development and existing challenges of construction health and safety to adapt to future new code of practice and challenges in this field. We mapped the topic landscape followed by identifying the salient development trajectories of this research area over time. We used the topic modeling algorithm to extract 10 distinct topics from 662 abstracts (filtered from a total of 895) of articles published between 1991 and 2020. In addition, we provided the most cited references and the most popular journal per topic as well. The results from a time series analysis suggested that the construction health and safety would maintain its popularity in the next 5 years. Research efforts would be devoted to the topics including “Physical health and disease”, “Migrant and race”, “Vocational ability and training”, and “Smart devices.” Among these topics, “Smart devices” would be the most promising one