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Clinical trials are crucial for the advancement of treatment and knowledge within the medical community. Since 2007, US federal government took the initiative and requires organizations sponsoring clinical trials with at least one site in the United States to submit information on these clinical trials to the ClinicalTrials.gov database, resulting in a rich source of information for clinical trial research. Nevertheless, only a handful of analytic studies have been carried out to understand this valuable data source. In this study, we propose to use network analysis to understand infectious disease clinical trial research. Our goal is to answer two important questions: (1) what are the concentrations and characteristics of infectious disease clinical trail research? and (2) how to accurately predict what type of clinical trials a sponsor (or an investigator) is interested in? The answers to the first question provide effective ways to summarize clinical trial research related to particular disease(s), and the answers to the second question help match clinical trial sponsors and investigators for information recommendation. By using 4,228 clinical trails as the test bed, our study involves 4,864 sponsors and 1,879 research areas characterized by Medical Subject Heading (MeSH) keywords. We extract a set of network measures to show patterns of infectious disease clinical trials, and design a new community based link prediction approach to predict sponsors' interests, with significant improvement compared to baselines. This trans-formative study concludes that using network analysis can tremendously help the understanding of clinical trial research for effective summarization, characterization, and prediction.