Graph Fairness Learning under Distribution Shifts

Graph neural networks (GNNs) have achieved remarkable performance on graph-structured data. However, GNNs may inherit prejudice from the training data and make discriminatory predictions based on sensitive attributes, such as gender and race. Recently, there has been an increasing interest in ensuring fairness on GNNs, but all of them are under the assumption that the training and testing data are under the same distribution, i.e., training data and testing data are from the same graph. Will graph fairness performance decrease under distribution shifts? How does distribution shifts affect graph fairness learning? All these open questions are largely unexplored from a theoretical perspective. To answer these questions, we first theoretically identify the factors that determine bias on a graph. Subsequently, we explore the factors influencing fairness on testing graphs, with a noteworthy factor being the representation distances of certain groups between the training and testing graph. Motivated by our theoretical analysis, we propose our framework FatraGNN. Specifically, to guarantee fairness performance on unknown testing graphs, we propose a graph generator to produce numerous graphs with significant bias and under different distributions. Then we minimize the representation distances for each certain group between the training graph and generated graphs. This empowers our model to achieve high classification and fairness performance even on generated graphs with significant bias, thereby effectively handling unknown testing graphs. Experiments on real-world and semi-synthetic datasets demonstrate the effectiveness of our model in terms of both accuracy and fairness.

Seismogram Transformer: A generic deep learning backbone network for multiple earthquake monitoring tasks

Seismic records, known as seismograms, are crucial records of ground motion resulting from seismic events, constituting the backbone of earthquake research and monitoring. The latest advancements in deep learning have significantly facilitated various seismic signal processing tasks. This paper introduces a novel backbone neural network model designed for various seismic monitoring tasks, named Seismogram Transformer (SeisT). Thanks to its efficient network architecture, SeisT matches or even outperforms the state-of-the-art models in earthquake detection, seismic phase picking, first-motion polarity classification, magnitude estimation, and azimuth estimation tasks, particularly in terms of out-of-distribution generalization performance. SeisT consists of multiple network layers composed of different foundational blocks, which help the model understand multi-level feature representations of seismograms from low-level to high-level complex features, effectively extracting features such as frequency, phase, and time-frequency relationships from input seismograms. Three different-sized models were customized based on these diverse foundational modules. Through extensive experiments and performance evaluations, this study showcases the capabilities and potential of SeisT in advancing seismic signal processing and earthquake research.