document: Event detection from evolution of click-through data Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data . In this paper , we propose the first approach to detect events from the click-through data , which is the log data of web search engines . The intuition behind event detection from click-through data is that such data is often event-driven and each event can be represented as a set ofquery-page pairs that are not only semantically similar but also have similar evolution pattern over time . Given the click-through data , in our proposed approach , we first segment it into a sequence of bipartite graphs based on theuser-defined time granularity . Next , the sequence of bipartite graphs is represented as a vector-based graph , which records the semantic and evolutionary relationships between queries and pages . After that , the vector-based graph is transformed into its dual graph , where each node is a query-page pair that will be used to represent real world events . Then , the problem of event detection is equivalent to the problem of clustering the dual graph of the vector-based graph . The clustering process is based on a two-phase graph cut algorithm . In the first phase , query-page pairs are clustered based on thesemantic-based similarity such that each cluster in the result corresponds to a specific topic . In the second phase , query-page pairs related to the same topic are further clustered based on the evolution pattern-based similarity such that each cluster is expected to represent a specific event under the specific topic . Experiments with real click-through data collected from a commercial web search engine show that the proposed approach produces high quality results .
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