Highest Degree Likelihood Search Algorithm Using a State Transition Matrix for Complex Networks

Since complex network theory was first put forward, the search issue for networks has drawn increasing attention from multidisciplinary researchers, and has played an important role in network study. Many practical applications require search algorithms such as searching for the shortest relationship link in social networks, seeking web sites on the Internet, and finding specified files in data sets. The key issue for these tasks is how to achieve a fast search. However, traditional random-walk-based methods cannot solve this problem effectively. In order to fast travel, a novel search algorithm which employs a highest degree likelihood approach with k hunters looking for the target simultaneously is presented for different types of complex networks. A state transition matrix is applied to explain this proposed method. We compare the proposed algorithm with the methods of forerunner in the simulation, and the results show that our algorithm performs more effectively. Finally, some applications and future challenges are discussed.