  Cross-device user matching is a critical problem in numerous domains,
including advertising, recommender systems, and cybersecurity. It involves
identifying and linking different devices belonging to the same person,
utilizing sequence logs. Previous data mining techniques have struggled to
address the long-range dependencies and higher-order connections between the
logs. Recently, researchers have modeled this problem as a graph problem and
proposed a two-tier graph contextual embedding (TGCE) neural network
architecture, which outperforms previous methods. In this paper, we propose a
novel hierarchical graph neural network architecture (HGNN), which has a more
computationally efficient second level design than TGCE. Furthermore, we
introduce a cross-attention (Cross-Att) mechanism in our model, which improves
performance by 5% compared to the state-of-the-art TGCE method.
