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Computation! Complexity Predict the Dynamic Structure • Percolation is a theoretical underpinning to model the Dynamics Structure of networks • (nodes = individuals, edges = relations) rumor diffusion epidemic spreading online information diffusion currency circulation
Problems • Predict the percolation transition point (Predictability, 可预测 ) – Purpose: • epidemiology and rumor spreading: to know when to make vaccinations to avoid the large scale epidemic or rumor spreading. • currency circulation: to help government make policies to avoid inflation. • online information diffusion: to master the rules of online information diffusion • Predict how fast is the outbreak at percolation transition point or whether the transition is controllable. (Controllability, 可调控 ) – Purpose: • epidemiology: to estimate how many people need to be vaccinate. • online information diffusion: to estimate the strength of a piece of information
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: Top-‐k Ranking Framework Top
Page 33 and 34: Challenges in the era of Big Data
Page 35 and 36: Can be useful on Social Networ
Page 37 and 38: Case1: Query the Real People o
Page 39 and 40: Summary • Three ScienAfic Chal

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