rise and fall patterns of information diffusion

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Rise and Fall Patterns of Information Diffusion:Model and Implications

Yasuko Matsubara (Kyoto University),

Yasushi Sakurai (NTT), B. Aditya Prakash (CMU),

Lei Li (UCB), Christos Faloutsos (CMU)

KDD’12, Beijing China

KDD 2012 1Y. Matsubara et al.

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• Meme (# 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 media

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Rise and fall patterns in social media

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• four classes on YouTube [Crane et al. ’08]• six classes on Meme [Yang et al. ’11]

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Rise and fall patterns in social media

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• Can 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]

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Rise and fall patterns in social media

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• Answer: YES!

• We can represent all patterns by single model

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Main idea - SpikeM- 1. Un-informed bloggers (uninformed about rumor)

- 2. External shock at time nb (e.g, breaking news)

- 3. Infection (word-of-mouth)

Time n=0 Time n=nb

β

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Infectiveness of a blog-post at age n:

- Strength of infection (quality of news)

- Decay function

Time n=nb+1

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- 1. Un-informed bloggers (uninformed about rumor)

- 2. External shock at time nb (e.g, breaking news)

- 3. Infection (word-of-mouth)

Time n=0 Time n=nb

β

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 - SpikeM

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-1.5 slope

J. G. Oliveira & A.-L. Barabási Human Dynamics: The Correspondence Patterns of Darwin and Einstein. Nature 437, 1251 (2005) . [PDF]

Response time (log)

Prob(RT > x)(log) -1.5

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SpikeM - with periodicity• Full equation of SpikeM

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Periodicity

noonPeak 3am

Dip

Time n

Bloggers change their activity over time

(e.g., daily, weekly, yearly)

activity

Details

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Details• Analysis – exponential rise and power-raw fall

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Lin-log

Log-log

Rise-part

SI -> exponential SpikeM -> exponential

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Details• Analysis – exponential rise and power-raw fall

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Lin-log

Log-log

Fall-part

SI -> exponential SpikeM -> power law

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Tail-part forecasts

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• SpikeM can capture tail part

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“What-if” forecasting

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e.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 release

C. Faloutsos (CMU)Google, June 2013

?

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“What-if” forecasting

14SpikeM can forecast upcoming spikes

(1) First spike

(2) Release date

(3) Two weeks before release

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Conclusions for spikes• Exp rise; PL decay• ‘spikeM’ captures all patterns, with a few

parms– And can do extrapolation– And forecasting

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Roadmap

• Graph problems:– G1: Fraud detection – BP– G2: Botnet detection – spectral – G3: Beyond graphs: tensors and ``NELL’’

• Influence propagation and spike modeling• Future research• Conclusions

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Challenge#1: Time evolving networks / tensors

• Periodicities? Burstiness?• What is ‘typical’ behavior of a node, over time• Heterogeneous graphs (= nodes w/ attributes)

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…

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Challenge #2: ‘Connectome’ – brain wiring

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• Which neurons get activated by ‘bee’• How wiring evolves• Modeling epilepsy

N. Sidiropoulos

George Karypis

V. Papalexakis

Tom Mitchell

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Thanks

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Thanks to: NSF IIS-0705359, IIS-0534205, CTA-INARC; Yahoo (M45), LLNL, IBM, SPRINT, Google, INTEL, HP, iLab

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Project info: PEGASUS

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www.cs.cmu.edu/~pegasusResults on large graphs: with Pegasus +

hadoop + M45

Apache license

Code, papers, manual, video

Prof. U Kang Prof. Polo Chau

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Cast

Akoglu, Leman

Chau, Polo

Kang, U

McGlohon, Mary

Tong, Hanghang

Prakash,Aditya

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Koutra,Danai

Beutel,Alex

Papalexakis,Vagelis

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References

• Deepayan Chakrabarti, Christos Faloutsos: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1): (2006)

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References• Christos Faloutsos, Tamara G. Kolda, Jimeng Sun:

Mining large graphs and streams using matrix and tensor tools. Tutorial, SIGMOD Conference 2007: 1174

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References• Yasuko Matsubara, Yasushi Sakurai, B. Aditya

Prakash, Lei Li, Christos Faloutsos, "Rise and Fall Patterns of Information Diffusion: Model and Implications", KDD’12, pp. 6-14, Beijing, China, August 2012

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References• Jimeng Sun, Dacheng Tao, Christos

Faloutsos: Beyond streams and graphs: dynamic tensor analysis. KDD 2006: 374-383

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Overall Conclusions• G1: fraud detection

– BP: powerful method– FaBP: faster; equally accurate; known

convergence

• G2: botnets -> Eigenspokes• G3: Subject-Verb-Object ->

Tensors/GigaTensor• Spikes: ‘spikeM’ (exp rise; PL drop)

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