Neighborhood Matchmaker Method: A Decentralized Optimization Algorithm for Personal Human Network

In this paper, we propose an algorithm called Neighborhood Matchmaker Method to optimize personal human networks. Personal human network is useful for various utilization of information like information gathering, but it is usually formed locally and often independently. In order to adapt various needs for information utilization, it is necessary to extend and optimize it. Using the neighborhood matchmaker method, we can increase a new friend who is expected to share interests via all own neighborhoods on the personal human network. Iteration of matchmaking is used to optimize personal human networks. We simulate the neighborhood matchmaker method with the practical data and the random data and compare the results by our method with those by the central server model. The neighborhood matchmaker method can reach almost the same results obtained by the sever model with each type of data.