A common and important query type in spatial networks is to find the Group K- Nearest neighbors "GKNN" of a given query objects set. given two sets of points. P and Q, the result of group Knearest neighbors (GKKN) query is the point(s) of P with the smallest sum of distances to all points in Q. due to the difficulties and the expense of the required calcu lations to answer such a query, almost all approaches in the literature use euclideam distances and not the real network distance. in this work we propose an approach that is based on a real network distance computation to answer the GKKN query. consider for instance three users at locations q1,q2 and q3 that want to find a metting point (e.g., ahotel); the corresponding query returns the data point p that minimizes the sum of network distances to q1,q2 and a3. our approach is based on manipulating the network voronoi diagram properties and applying a progressive incremental network expansion for finding the polygon inner network distances required to solve a GKKN query