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Recently, a growing number of researches have focused on the issues raised by the knowledge discovery of online information, particularly the problems of tracking topics, ideas, and users' spreading influence across the Web. In this paper, the search-engine query logs on Topic Detection and Tracking (TDT) is analyzed other than study of the quality of the search result or query recommendation. By constructing a novel bi-type heterogeneous query graph, the queries' semantic similarity and query-URL relation are combined together. Utilizing social network analysis (SNA) method to analyze the query graph with optimization of the community discovery algorithm LPA by grouping the nodes who are linked with the same URL initially, we can find the topics in the query logs. To evaluate the topic evolution pattern, we group the similar communities over each adjacent time stamps into clusters. Extensive experiments demonstrate the effectiveness and efficiency of the methods.