Individuals communicate with each other strategically to improve their access to information and to capitalize on social connections in attaining personal and professional goals. Yet, we know little about how specific networking strategies impact the efficiency of communication networks both at a global and local level. Here, we perform data-driven computer simulations that examine the effect of two predominant networking strategies: (i) structural change, involving addition and deletion of communication channels and (ii) frequency change, involving increase or decrease of communication on existing channels. In our proposed framework, these two strategies encompass the spectrum of exploring new connections and exploiting existing ones, and are implemented based on the generic social processes of interaction reciprocity and triadic closure. Three main results emerge from our simulations. First, our structural and frequency change strategies designed to reflect human behavior differ from null models represented by random strategies. Second, they have distinct effects on global and local efficiency. Third, these strategies work consistently across heterogeneous network structures and various network evolution scenarios. Taken together, our findings reassess conventional wisdom about the effectiveness of networking strategies and introduce novel frameworks to study the impact of networking via modeling approaches informed by social and communication theory.