Recent studies have shown that analysis of the eye tracking data is a viable way to understand the cognitive decision making process of people undertaking visual search tasks. As a result, its becomes important to develop new methods for analyzing the eye tracking data obtained in such scenarios. Visualization of data is a crucial stage in the analysis process. The prevalent eye fixation data visualization processes suffer from mainly two kinds of limitations, firstly; they are not efficient for large number of targets and secondly, they are unable to handle change in both the positions and the number of targets visible on the display. Another major shortcoming of the present methods’ is the absence of quantitative metrics for advance analysis of the eye movement data. The present study tries to address the above mentioned limitations by adapting the directed weighted network (DWN) methodology and its associated centrality metrics to develop a new visualization and analysis tool. A pilot study which simulated the realistic air traffic control task environment was performed to demonstrate the developed methodology. The results obtained are very promising, as the method was able to identify the important targets (aircraft) interrogated by the air traffic controller based on different time frames. The obtained result lays the first stepping stone in the development of an effective data visualization method and also quantitative metrics for analyzing complex eye movements for a multi-element tracking task.