  The study of multi-type Protein-Protein Interaction (PPI) is fundamental for
understanding biological processes from a systematic perspective and revealing
disease mechanisms. Existing methods suffer from significant performance
degradation when tested in unseen dataset. In this paper, we investigate the
problem and find that it is mainly attributed to the poor performance for
inter-novel-protein interaction prediction. However, current evaluations
overlook the inter-novel-protein interactions, and thus fail to give an
instructive assessment. As a result, we propose to address the problem from
both the evaluation and the methodology. Firstly, we design a new evaluation
framework that fully respects the inter-novel-protein interactions and gives
consistent assessment across datasets. Secondly, we argue that correlations
between proteins must provide useful information for analysis of novel
proteins, and based on this, we propose a graph neural network based method
(GNN-PPI) for better inter-novel-protein interaction prediction. Experimental
results on real-world datasets of different scales demonstrate that GNN-PPI
significantly outperforms state-of-the-art PPI prediction methods, especially
for the inter-novel-protein interaction prediction.
