Abstract
Graph machine learning algorithms are being commonly applied to a broad range of prediction tasks in systems biology. 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 (Common Neighbor, Adamic Adar, Random Walk with Restart, Von Neumann, Resource Allocation, Hub-Depressed Index, Hub-Promoted Index, and adjacency matrix) 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 the integration of multiple similarity measures improves the robustness of the framework, especially when the input networks are sparse.