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Phys. Rev. E, 87, 052130, 2013 We find some typical features for discontinuous percolation transitions which help us quickly knows a information is unpredictable & uncontrollable Lack of finite size scaling ! EPL, 100 (6), 66006 Multiple giant components maybe appear ! A second percolation transition may occur ! Phys. Rev. Lett. 106, 115701, 2011.
Can be useful on Social Networks • Modeling the information diffusion in online social networks. • Predict the explosive phase transition of online information diffusion. • Investigate the ways to make a unpredictable and unctrollable system more predictable and controllable
Page 1 and 2: Big Data: Science or Gimmick! 程
Page 3 and 4: Is Big Data Big Value Predicti
Page 5 and 6: Big Challenges Exist in Big Da
Page 7 and 8: Topics in this talk Data Comple
Page 9 and 10: Topic Modeling meets Big Data
Page 11 and 12: Sparse and Meaningful Topics • L
Page 13 and 14: Group Sparse Topical Coding Group
Page 15 and 16: Results • Datasets - 20-newsgrou
Page 17 and 18: Short texts are prevalent Uncov
Page 19 and 20: Key idea of our approach • T
Page 21 and 22: Comparison between different models
Page 23 and 24: Evaluation on Baidu Zhidao • Dat
Page 25 and 26: Ranking meets Big Data High com
Page 27 and 28: Top-‐k Learning to Rank Ran
Page 29 and 30: Top-‐k Ranking Framework Top
Page 31 and 32: Problems • Predict the percolati
Page 33: Challenges in the era of Big Data
Page 37 and 38: Case1: Query the Real People o
Page 39 and 40: Summary • Three ScienAfic Chal

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