The present invention can increase the odds of choosing the right people for a team by considering their “rate of interest / knowledge” in multiple topics. Given a known contacts network, represented by nodes interconnected by links, several different sub-networks are identified within it, corresponding to different topics or areas of expertise required to a specific project. For each sub-network, there will be nodes with an associated grade, based on that person's knowledge / interest for the topic related to that sub-network. As such, each node / person receives a grade for each topic. Using these grades, a weight of each link between the nodes is calculated. This process is performed for every node for each topic and associated grade. After that, a superposition of all sub-networks is made and a multiple interest network is yielded.