Weighted clustering coefficient maximization for air transportation networks

In transportation networks the robustness of a network regarding nodes and links failures is a key factor for its design. At the same time, traveling passengers usually prefer the itinerary with fewer legs. The average clustering coefficient can be used to measure the robustness of a network. A high average clustering coefficient is often synonymous with a lower average travel distance and fewer number of legs. In this paper we present the average weighted clustering coefficient maximization problem, and give several solution methods based on branch and bound algorithm, dynamic programming and quadratically constrained programs.