Knowledge is the keynote for innovation and economic growth. The effective management of organizational knowledge networks is the key for attaining competitive advantage. Applications of knowledge networks analysis already include several knowledge-intensive firms, such as consulting firms, manufacturing firms, telecommunications firms, healthcare and pharmaceuticals industry, biotechnology industry, banks and financial services companies, petroleum companies, etc. . The position of “experts” (highly knowledgeable agents) within the organizational knowledge network, may influence significantly the emerging knowledge dynamics, resulting in faster or slower knowledge diffusion within the network. We investigate the emerging knowledge dynamics in 3 typical network structures (Random, Small-World, Scale-Free). At each time step, each agent κ selects a neighbor λ for knowledge acquisition. The selecting agent κ will obtain some new knowledge, only if the selected neighbor λ has higher knowledge level, otherwise there is no knowledge gain for κ. We address two questions: (Q1) What are the advantages of positioning experts on central nodes, compared to random positioning? Central positions have high centralities (Degree, Closeness, Betweenness, Eigen-centrality). (Q2) Agents aware of the knowledge of their neighbors, may first “filter” out their neighbors of lower knowledge and then “select” some neighbor with higher knowledge. What is the impact of “selecting” after “filtering”, compared to the conventional order of “selecting” before “filtering”? The policy of “selecting” after “filtering” is found to be significantly more efficient. The non-commutativity of “selection” and “filtering” reveals a Non-Boolean Logic, underlying network operations . Moreover, “intelligent selections” (“selecting” after “filtering”) result in faster knowledge spread, regardless of the location of experts within the network .