  Identifying unique characteristics in a network through comparison with
another network is an essential network analysis task. For example, with
networks of protein interactions obtained from normal and cancer tissues, we
can discover unique types of interactions in cancer tissues. This analysis task
could be greatly assisted by contrastive learning, which is an emerging
analysis approach to discover salient patterns in one dataset relative to
another. However, existing contrastive learning methods cannot be directly
applied to networks as they are designed only for high-dimensional data
analysis. To address this problem, we introduce a new analysis approach called
contrastive network representation learning (cNRL). By integrating two machine
learning schemes, network representation learning and contrastive learning,
cNRL enables embedding of network nodes into a low-dimensional representation
that reveals the uniqueness of one network compared to another. Within this
approach, we also design a method, named i-cNRL, which offers interpretability
in the learned results, allowing for understanding which specific patterns are
only found in one network. We demonstrate the effectiveness of i-cNRL for
network comparison with multiple network models and real-world datasets.
Furthermore, we compare i-cNRL and other potential cNRL algorithm designs
through quantitative and qualitative evaluations.
