This paper focuses on ‘user browsing graph’ which is constructed with users’ click-through behavior modeled with Web access logs. User browsing graph has recently been adopted to improve Web search performance and the initial study shows it is more reliable than hyperlink graph for inferring page importance. However, structure and evolution of the user browsing graph haven’t been fully studied and many questions remain to be answered. In this paper, we look into the structure of the user browsing graph and its evolution over time. We try to give a quantitative analysis on the difference in graph structure between hyperlink graph and user browsing graph, and then find out why link analysis algorithms perform better on the browsing graph. We also propose a method for combining user behavior information into hyper link graph. Experimental results show that user browsing graph and hyperlink graph share few links in common and a combination of these two graphs can gain good performance in quality estimation of pages.