Many interconnected systems and particularly social interactions can be modeled as networks. These networks often exhibit common properties such as high clustering coefficient, low average path lengths and degree distributions following power-law. Networks having these properties are called small world-scale free networks or simply complex networks. Recent interest in complex networks has catalysed the development of algorithmic models to artificially generate these networks. Often these algorithms introduce network properties in the model regardless of their social interpretation resulting in networks which are statistically similar but structurally different from real world networks. In this paper, we focus on social networks and apply concepts of social ties, homophily and extraversion-introversion to develop a model for social networks with small world and scale free properties. We claim that the proposed model produces networks which are structurally similar to real world social networks.