Semantic memory refers to the storage of word meanings, natural and artificial concepts and general world knowledge. In the past two decades several computational models were developed to provide insight into semantic memory. One family of computational models is network science theories which are proving to be beneficial in explaining the organization and dynamics of cognitive and behavioral processes. The current talk will present the results of several studies which used network science tools to investigate structure and processes of semantic memory. Specifically, based on network science methodology we examine a computational alternative to computing semantic distance in semantic memory. This approach also addresses key issues in cognitive theory such as the effect of semantic distance on spreading activation processes and memory retrieval. In addition, we will present work in which network science methodology was implemented to study processes involved in creative ability. The findings demonstrate the utility of network science in examining high-level cognitive phenomena such as creativity and intelligence.