Because of the current energy crisis, researchers and governments pay more attention to new and renewable energy. Renewable energy is a new area of development in China, and thus many talents are attracted to pursue research in these directions with the result that the renewable energy field has an urgent need to understand how the various talents are divided within the field and the statistical findings that can be determined from this. Based on this division and the statistical information it is possible to make decisions on how to plan for the renewable energy field's development in areas, for example, such as resource delivery. To begin with we can create a statistical map of the talents' social network based on the talent carriers and their research directions. Then we use top-k talents to represent the network and divide the network into k parts. Analyzing the properties of a single point's neighbors and the transition probability of these points and those around them, we propose the value of Neighbor Rank (NR). By taking the point's gravity into account we put forward the Gravity Rank (GR). We then combine these 2 models. The experiment not only gives a reasonable result but also the k parts are always the k main areas of the renewable energy field. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.