This book is the outcome of a conference on “Partnerships and the Spread of HIV and Other Infections” sponsored by the International Union for the Scientific Study of Population (IUSSP) in Chiang Mai, Thailand in February 2000. The purpose of the conference was to synthesize a decade's worth of new empirical research that has used network analytic methods to understand the population dynamics of HIV, and make it accessible to the larger research community. Interest in social network analysis has grown rapidly among applied researchers in the population sciences—starting with epidemiologists working on the HIV/AIDS epidemic in the mid 1980s and moving quickly into many other areas in demography. Methods for network analysis have been under development during the past 50 years (Wasserman and Faust 1994), and the theory is rooted in the classic works of anthropology (e.g. Levi-Strauss 1969), but it is only in the last decade or so that this work is beginning to find a broader audience among applied researchers. A good example is the academic and popular attention now given to “small world” diffusion models (Watts 1999; Barabasi 2002; Watts 2003), the variations on the “six degrees of separation” game (see, e.g. www.cs. virginia.edu/oracle), and the range of computer viruses that regularly make their way into our email inboxes. As a result of this new attention, the pace of progress in the field of network analysis has increased, and the volume and range of new work coming out in this area is now quite remarkable. Conducting empirical studies of networks, however, remains quite a challenge. It requires many changes in research design, and there is currently no source in the published literature that an interested researcher could turn to for a systematic introduction to these issues. The Chiang Mai conference was set up to produce such a handbook, and this is the result. The conference was explicitly organized to ensure that the presentations covered the range of issues relevant for network research: from the impact it has on data collection instruments and sampling, to the changes it requires in statistical methodology, and finally, to what we have learned from network studies in perhaps the most active research context to use these tools: the epidemic spread of HIV