  Power control for the device-to-device interference channel with
single-antenna transceivers has been widely analyzed with both model-based
methods and learning-based approaches. Although the learning-based approaches,
i.e., datadriven and model-driven, offer performance improvement, the widely
adopted graph neural network suffers from learning the heterophilous power
distribution of the interference channel. In this paper, we propose a deep
learning architecture in the family of graph transformers to circumvent the
issue. Experiment results show that the proposed methods achieve the
state-of-theart performance across a wide range of untrained network
configurations. Furthermore, we show there is a trade-off between model
complexity and generality.
