The usage of the term Knowledge Graph (KG) has gained significant popularity since 2012, when Google introduced its own knowledge graph, and how they used it to enhance their searches and question answering systems. While various definitions and interpretations for knowledge graphs have been presented, what remains consistent is that knowledge graphs are commonly used with reasonsers to make inferences about data, based on assertions and axioms written by human experts. But knowledge graphs, which store complex, multi-dimensional data contain hidden patterns and trends that cannot be explored simply using reasoners. In such a case it becomes necessary to extract parts of the knowledge graph (focusing on the instances related to one property at a time) and analyze them individually in order to conduct a focused but tractable exploration of the domain. In this presentation, we present one way to gain insights from knowledge graphs, using network science. To achieve this goal, we have formalised the partitioning of knowledge graphs to unipartite knowledge networks, and present various ways to explore and analyse such knowledge networks to form scientific hypotheses, gain scientific insights and make discoveries.