Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach

Modeling complex data, e.g. time series as well as network-based data, is a prominent area of research. In this paper, we focus on a combination of both, analyzing network-based spatial sensor data which is attributed with high frequency time series information. We apply a symbolic representation and an attention-based local abstraction approach, to enhance interpretability on the respective complex high frequency time series data. For this, we aim at identifying informative measurements captured by the respective nodes of the sensor network. To do so, we demonstrate the efficacy of the Symbolic Fourier Approximation (SFA) and the attention-based symbolic abstraction method to localize relevant node sensor-information, by using a transformer architecture as an encoder for a graph neural network. In our experiments, we compare two seismological datasets to their previous state-of-the-art model, demonstrating the advantages and benefits of our presented approach.