Discovering the Most Potential Stars in Social Networks with Infra-skyline Queries

With the rapid development of Social Network (SN for short), people increasingly pay attention to the importance of the roles which they play in the SNs. As is usually the case, the standard for measuring the importance of the members is multi-objective. The skyline operator is thus introduced to distinguish the important members from the entire community. For decision-making, people are interested in the most potential members which can be promoted into the skyline with minimum cost, namely the problem of Member Promotion in Social Networks. In this paper, we propose some interesting new concepts such as Infra-Skyline and Promotion Boundary, and then we exploit a novel promotion boundary based approach, i.e., the InfraSky algorithm. Extensive experiments on both real and synthetic datasets are conducted to show the effectiveness and efficiency of the InfraSky algorithm.

[1]  Xiang Lian,et al.  Dynamic skyline queries in metric spaces , 2008, EDBT '08.

[2]  Heng Tao Shen,et al.  Multi-source Skyline Query Processing in Road Networks , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[3]  Bernhard Seeger,et al.  Progressive skyline computation in database systems , 2005, TODS.

[4]  Jiawei Han,et al.  Mining Thick Skylines over Large Databases , 2004, PKDD.

[5]  Lei Zou,et al.  Dynamic Skyline Queries in Large Graphs , 2010, DASFAA.

[6]  Donald Kossmann,et al.  Shooting Stars in the Sky: An Online Algorithm for Skyline Queries , 2002, VLDB.

[7]  Jiawei Han,et al.  Promotion Analysis in Multi-Dimensional Space , 2009, Proc. VLDB Endow..

[8]  Jae Soo Yoo,et al.  Skyline Minimum Vector , 2010, 2010 12th International Asia-Pacific Web Conference.

[9]  Donald Kossmann,et al.  The Skyline operator , 2001, Proceedings 17th International Conference on Data Engineering.

[10]  Cyrus Shahabi,et al.  The spatial skyline queries , 2006, VLDB.

[11]  Bernhard Seeger,et al.  An optimal and progressive algorithm for skyline queries , 2003, SIGMOD '03.

[12]  Beng Chin Ooi,et al.  Efficient Progressive Skyline Computation , 2001, VLDB.

[13]  David Fuhry,et al.  Efficient skyline computation in metric space , 2009, EDBT '09.

[14]  Dino Pedreschi,et al.  Knowledge Discovery in Databases: PKDD 2004 , 2004, Lecture Notes in Computer Science.