Title: Graph Mixing Additive Networks
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph Deep Learning, Graph Neural Networks, Interpretability

Abstract: Real-world temporal data often consists of multiple signal types recorded at irregular, asynchronous intervals. For instance, in the medical domain, different types of blood tests can be measured at different times and frequencies, resulting in fragmented and unevenly scattered temporal data. Similar issues of irregular sampling occur in other domains, such as the monitoring of large systems using event log files. Effectively learning from such data requires handling sets of temporally sparse and heterogeneous signals. In this work, we propose Graph Mixing Additive Networks (GMAN), a novel and interpretable-by-design framework for learning directly from sets of graphs that represent such signals.
GMAN provides diverse interpretability capabilities, including node-level, graph-level, and subset-level importance, and enables practitioners to trade finer-grained interpretability for greater expressivity when domain priors are available.
GMAN achieves state-of-the-art performance in real-world high-stakes tasks, including predicting Crohn’s disease onset and hospital length of stay from routine blood test measurements and detecting fake news. Furthermore, we demonstrate how GMAN’s interpretability properties assist in revealing disease development phase transitions and provide crucial insights in the healthcare domain.

TL;DR: We present a novel interpretable graph learning model for learning over sets of temporally-sparse data