Canonical Representation of Biological Networks Using Graph Convolution

Graph machine learning algorithms are being commonly applied to a broad range of prediction tasks in systems biology. These algorithms present many design choices depending on the specific application and available data, making it difficult to choose from different options. An important design criterion in this regard is the definition of "topological similarity" between two nodes in a network, which is used to design convolution matrices for graph convolution or loss functions to evaluate node embeddings. Many measures of topological similarity exist in network science literature (e.g., random walk based proximity, shared neighborhood) and recent comparative studies show that the choice of topological similarity can have a significant effect on the performance and reliability of graph machine learning models. We propose GRAPHCAN, a framework for computing canonical representations for biological networks using a similarity-based Graph Convolutional Network (GCN). GRAPHCAN integrates multiple node similarity measures to compute canonical node embeddings for a given network. The resulting embeddings can be utilized directly for downstream machine learning tasks. We comprehensively evaluate GRAPHCAN in the context of various link prediction tasks in systems biology. Our results show that GRAPHCAN consistently delivers improved prediction accuracy over algorithms that directly use the adjacency matrix of the input network, and the integration of multiple similarity measurements improves the robustness of the framework. The implementation of GRAPHCAN can be found in https://github.com/Meng-zhen-Li/Similarity-based-GCN.git.