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Rise and Fall Patterns of Information Diffusion

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Rise and Fall Patterns of Information Diffusion: Model and Implications

Rise and Fall Patterns of Information Diffusion:Model and ImplicationsYasuko Matsubara (Kyoto University), Yasushi Sakurai (NTT), B. Aditya Prakash (CMU), Lei Li (UCB), Christos Faloutsos (CMU)KDD12, Beijing ChinaKDD 20121Y. Matsubara et al.CMU SCSMeme (# of mentions in blogs)short phrases Sourced from U.S. politics in 2008

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you can put lipstick on a pig yes we can Rise and fall patterns in social mediaC. Faloutsos (CMU)Google, June 2013CMU SCS

Rise and fall patterns in social media3four classes on YouTube [Crane et al. 08]six classes on Meme [Yang et al. 11]

C. Faloutsos (CMU)Google, June 2013CMU SCS

Rise and fall patterns in social media4Can we find a unifying model, which includes these patterns?four classes on YouTube [Crane et al. 08]six classes on Meme [Yang et al. 11]

C. Faloutsos (CMU)Google, June 2013CMU SCSRise and fall patterns in social media5Answer: YES!

We can represent all patterns by single model

C. Faloutsos (CMU)Google, June 2013CMU SCS6Main idea - SpikeM1. Un-informed bloggers (uninformed about rumor)2. External shock at time nb (e.g, breaking news)3. Infection (word-of-mouth)

Time n=0Time n=nbC. Faloutsos (CMU)Google, June 2013 Infectiveness of a blog-post at age n: Strength of infection (quality of news)Decay function

Time n=nb+1CMU SCS671. Un-informed bloggers (uninformed about rumor)2. External shock at time nb (e.g, breaking news)3. Infection (word-of-mouth)

Time n=0Time n=nb

C. Faloutsos (CMU)Google, June 2013 Infectiveness of a blog-post at age n: Strength of infection (quality of news)Decay function

Time n=nb+1

Main idea - SpikeMCMU SCS7Google, June 2013C. Faloutsos (CMU)8-1.5 slopeJ. G. Oliveira & A.-L. Barabsi Human Dynamics: The Correspondence Patterns of Darwin and Einstein. Nature 437, 1251 (2005) . [PDF] Response time (log)Prob(RT > x)(log)

-1.5CMU SCSSpikeM - with periodicityFull equation of SpikeM

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Periodicity

noonPeak3amDipTime nBloggers change their activity over time(e.g., daily, weekly, yearly)activity

DetailsC. Faloutsos (CMU)Google, June 2013CMU SCSDetailsAnalysis exponential rise and power-raw fall

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Lin-logLog-log Rise-part

SI -> exponential SpikeM -> exponential

C. Faloutsos (CMU)Google, June 2013CMU SCSDetailsAnalysis exponential rise and power-raw fall

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Lin-logLog-log Fall-part

SI -> exponential SpikeM -> power law

C. Faloutsos (CMU)Google, June 2013CMU SCS11Tail-part forecasts12SpikeM can capture tail part

C. Faloutsos (CMU)Google, June 2013CMU SCSWhat-if forecasting13e.g., given (1) first spike, (2) release date of two sequel movies (3) access volume before the release date

?(1) First spike(2) Release date(3) Two weeks before releaseC. Faloutsos (CMU)Google, June 2013?CMU SCSWhat-if forecasting14SpikeM can forecast upcoming spikes

(1) First spike(2) Release date(3) Two weeks before releaseC. Faloutsos (CMU)Google, June 2013CMU SCSConclusions for spikesExp rise; PL decayspikeM captures all patterns, with a few parmsAnd can do extrapolationAnd forecastingGoogle, June 2013C. Faloutsos (CMU)15CMU SCSC. Faloutsos (CMU)16RoadmapGraph problems:G1: Fraud detection BPG2: Botnet detection spectral G3: Beyond graphs: tensors and ``NELLInfluence propagation and spike modelingFuture researchConclusionsGoogle, June 2013

CMU SCSChallenge#1: Time evolving networks / tensorsPeriodicities? Burstiness?What is typical behavior of a node, over timeHeterogeneous graphs (= nodes w/ attributes)Google, June 2013C. Faloutsos (CMU)17

CMU SCSChallenge #2: Connectome brain wiringGoogle, June 2013C. Faloutsos (CMU)18Which neurons get activated by beeHow wiring evolvesModeling epilepsy

N. SidiropoulosGeorge KarypisV. PapalexakisTom MitchellCMU SCSC. Faloutsos (CMU)19ThanksGoogle, June 2013Thanks to: NSF IIS-0705359, IIS-0534205, CTA-INARC; Yahoo (M45), LLNL, IBM, SPRINT, Google, INTEL, HP, iLab

CMU SCSFaloutsos et al8/31/201319C. Faloutsos (CMU)20Project info: PEGASUS

Google, June 2013www.cs.cmu.edu/~pegasusResults on large graphs: with Pegasus + hadoop + M45Apache licenseCode, papers, manual, videoProf. U Kang

Prof. Polo ChauCMU SCSFaloutsos et al8/31/201320C. Faloutsos (CMU)21Cast

Akoglu, LemanChau, PoloKang, UMcGlohon, Mary

Tong, Hanghang

Prakash,AdityaGoogle, June 2013

Koutra,Danai

Beutel,AlexPapalexakis,Vagelis

CMU SCSFaloutsos et al8/31/201321C. Faloutsos (CMU)22ReferencesDeepayan Chakrabarti, Christos Faloutsos: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1): (2006)

Google, June 2013CMU SCSFaloutsos22C. Faloutsos (CMU)23ReferencesChristos Faloutsos, Tamara G. Kolda, Jimeng Sun: Mining large graphs and streams using matrix and tensor tools. Tutorial, SIGMOD Conference 2007: 1174

Google, June 2013CMU SCSFaloutsos23ReferencesYasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, Christos Faloutsos, "Rise and Fall Patterns of Information Diffusion: Model and Implications", KDD12, pp. 6-14, Beijing, China, August 2012Google, June 2013C. Faloutsos (CMU)24CMU SCSReferencesJimeng Sun, Dacheng Tao, Christos Faloutsos: Beyond streams and graphs: dynamic tensor analysis. KDD 2006: 374-383

Google, June 2013C. Faloutsos (CMU)25CMU SCSOverall ConclusionsG1: fraud detectionBP: powerful methodFaBP: faster; equally accurate; known convergenceG2: botnets -> EigenspokesG3: Subject-Verb-Object -> Tensors/GigaTensorSpikes: spikeM (exp rise; PL drop)

Google, June 2013C. Faloutsos (CMU)26

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