Netpro2vec: a graph embedding technique based on probability distribution representations of graphs and skip-gram learning model

Netpro2vec is a neural embedding framework, based on probability distribution representations of graphs. The goal is to look at node descriptions, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.