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CMU SCS
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.
CMU SCS
• Meme (# of mentions in blogs)– short phrases Sourced from U.S. politics in 2008
2
“you can put lipstick on a pig”
“yes we can”
Rise and fall patterns in social media
C. Faloutsos (CMU)Google, June 2013
CMU SCS
Rise and fall patterns in social media
3
• four classes on YouTube [Crane et al. ’08]• six classes on Meme [Yang et al. ’11]
C. Faloutsos (CMU)Google, June 2013
CMU SCS
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]
C. Faloutsos (CMU)Google, June 2013
CMU SCS
Rise and fall patterns in social media
5
• Answer: YES!
• We can represent all patterns by single model
C. Faloutsos (CMU)Google, June 2013
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6
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
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
CMU SCS
7
- 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
CMU SCS
Google, June 2013 C. Faloutsos (CMU) 8
-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
CMU SCS
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
C. Faloutsos (CMU)Google, June 2013
CMU SCS
Details• Analysis – exponential rise and power-raw fall
10
Lin-log
Log-log
Rise-part
SI -> exponential SpikeM -> exponential
C. Faloutsos (CMU)Google, June 2013
CMU SCS
Details• Analysis – exponential rise and power-raw fall
11
Lin-log
Log-log
Fall-part
SI -> exponential SpikeM -> power law
C. Faloutsos (CMU)Google, June 2013
CMU SCS
Tail-part forecasts
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• SpikeM can capture tail part
C. Faloutsos (CMU)Google, June 2013
CMU SCS
“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
?
CMU SCS
“What-if” forecasting
14SpikeM can forecast upcoming spikes
(1) First spike
(2) Release date
(3) Two weeks before release
C. Faloutsos (CMU)Google, June 2013
CMU SCS
Conclusions for spikes• Exp rise; PL decay• ‘spikeM’ captures all patterns, with a few
parms– And can do extrapolation– And forecasting
Google, June 2013 C. Faloutsos (CMU) 15
CMU SCS
C. Faloutsos (CMU) 16
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
Google, June 2013
CMU SCS
Challenge#1: Time evolving networks / tensors
• Periodicities? Burstiness?• What is ‘typical’ behavior of a node, over time• Heterogeneous graphs (= nodes w/ attributes)
Google, June 2013 C. Faloutsos (CMU) 17
…
CMU SCS
Challenge #2: ‘Connectome’ – brain wiring
Google, June 2013 C. Faloutsos (CMU) 18
• Which neurons get activated by ‘bee’• How wiring evolves• Modeling epilepsy
N. Sidiropoulos
George Karypis
V. Papalexakis
Tom Mitchell
CMU SCS
C. Faloutsos (CMU) 19
Thanks
Google, June 2013
Thanks to: NSF IIS-0705359, IIS-0534205, CTA-INARC; Yahoo (M45), LLNL, IBM, SPRINT, Google, INTEL, HP, iLab
CMU SCS
C. Faloutsos (CMU) 20
Project info: PEGASUS
Google, June 2013
www.cs.cmu.edu/~pegasusResults on large graphs: with Pegasus +
hadoop + M45
Apache license
Code, papers, manual, video
Prof. U Kang Prof. Polo Chau
CMU SCS
C. Faloutsos (CMU) 21
Cast
Akoglu, Leman
Chau, Polo
Kang, U
McGlohon, Mary
Tong, Hanghang
Prakash,Aditya
Google, June 2013
Koutra,Danai
Beutel,Alex
Papalexakis,Vagelis
CMU SCS
C. Faloutsos (CMU) 22
References
• Deepayan Chakrabarti, Christos Faloutsos: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1): (2006)
Google, June 2013
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C. Faloutsos (CMU) 23
References• Christos Faloutsos, Tamara G. Kolda, Jimeng Sun:
Mining large graphs and streams using matrix and tensor tools. Tutorial, SIGMOD Conference 2007: 1174
Google, June 2013
CMU SCS
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
Google, June 2013 C. Faloutsos (CMU) 24
CMU SCS
References• Jimeng Sun, Dacheng Tao, Christos
Faloutsos: Beyond streams and graphs: dynamic tensor analysis. KDD 2006: 374-383
Google, June 2013 C. Faloutsos (CMU) 25
CMU SCS
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)
Google, June 2013 C. Faloutsos (CMU) 26