The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Proceedings of the 2006 international workshop on Mining software repositories
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
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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.