Wearable sensing platforms like modern smart phones have proven to be effective means in the complexity and computational social sciences. This paper draws from explicit (phone calls, SMS messaging) and implicit (proximity sensing based on Bluetooth radio signals) interaction patterns collected via smart phones and reality mining techniques to explain the dynamics of personal interactions and relationships. We consider three real human to human interaction networks, namely physical proximity, phone communication and instant messaging. We analyze a real undergraduate community's social circles and consider various topologies, such as the interaction patterns of users with the entire community, and the interaction patterns of users within their own community. We fit distributions of various interactions, for example, showing that the distribution of users that have been in physical proximity but have never communicated by phone fits a gaussian. Finally, we consider five types of relationships, for example friendships, to see whether significant differences exist in their interaction patterns. We find statistically significant differences in the physical proximity patterns of people who are mutual friends and people who are non-mutual (or asymmetric) friends, though this difference does not exist between mutual friends and never friends, nor does it exist in their phone communication patterns. Our findings impact a wide range of data-driven applications in socio-technical systems by providing an overview of community interaction patterns which can be used for applications such as epidemiology, or in understanding the diffusion of opinions and relationships.