Research has shown that social isolation is a serious health risk issue which has unignorably negative impacts on well-being and quality of life. It is also harmful to healthy human development and a high-risk public health issue. Given the rapid growth of aging populations around the world, the problem has gained growing attention as a public health priority as seniors are among the most vulnerable to social isolation. In this research work, we define and model the concept of social isolation in a computational form and introduce new quality functions to measure it. Moreover, we propose a novel algorithm to identify socially isolated individuals in a community using social network analysis techniques. The performance and efficiency of our proposed approach are evaluated using various synthetic social graphs.