Understanding the interaction mechanism between host proteins and the Ebola virus is essential for targeted drug therapeutics. Although a spectrum of studies dealing with host protein and Ebola virus are available, the protein-protein interactions (PPIs) databases are not dense and thus involve bias toward well-studied proteins. Furthermore, the majority of these studies consider the supervised learning approach, whereas a sufficiently large labeled dataset is not available. In this study, we present an unsupervised computational framework to predict the associations between non-interacting Ebola-human protein pairs. We begin the work by constructing a bipartite graph from the known Ebola-human PPI dataset and then employ a low-dimensional embedding technique, Node2Vec, to understand the structural and relational characteristics of the proteins. A clustering algorithm is then applied to the human protein embeddings to unveil significant modules, which are validated through functional enrichment analysis. From these clusters, bipartite graphs are reconstructed, including known Ebola-human proteins, and further analyzed using Node2Vec and cosine similarity. Our framework successfully predicts new PPIs, many of which are indirectly supported by literature evidence. The suggested method can be used for various host-pathogen interaction investigations and provides a data-driven strategy for systematic PPI identification.