Social ties defined by phone calls made between people can be grouped to various affinity networks, such as family members, utility network, friends, coworkers, etc. An understanding of call behavior within each social affinity network and the ability to infer the type of a social tie from call patterns is invaluable for various industrial purposes. For example, the telecom industry can use such information for consumer retention, targeted advertising, and customized services. In this paper, we analyze the patterns of 4.3 million phone call data records produced by 360,000 subscribers from two California cities. Our findings can be summarized as follows. We reveal significant differences among different affinity networks in terms of different call attributes. For example, members within the family network generate the highest average number of calls. Despite the differences between the two cities, for a given affinity network they show similar phone call behaviors. We identify specific features that model statistically meaningful changes in call patterns and can be used for prediction and classification of affinity networks, and we also find correlations between the features associated with call behavior. For example, when subscribers call each other after a long time, their calls tend to take longer. This knowledge leads to discussions of proper machine learning classification approaches as well as promising applications in telecom and security.