Summary: With more and more new social network services appearing, the volumes of data they created are continuous increasing at an astonishing speed. These data represent a snapshot of what real social network happening and evolving, and they contain the basic relationships and interacted behaviors among users. Core-based friend cycles are connected nodes around given “core node”, and their interaction pattern with core node may reveal potential habits of users. This may be useful for online personalized advertising, online public opinion analysis, and other fields. To search core-based friend cycles by global method needs to scan the entire graph of social network every time, and thus its efficiency is low. This study (1) modeled the core-based friend cycles with core-based subgraphs; (2) provided algorithms to find structure and evolving interaction pattern of friend cycles around a given core node in online social network; (3) discussed and analyzed the design of incremental search algorithm theoretically; (4)applied the provided model to do informed prediction between node and its core-based friend cycles and received hit rate over 77.6\%; (5) provided sufficient experiments and proven the newly proposed approach with good scalability and efficiency.