Making Diffusion Work for You: From Social Media to

EpidemiologyB. Aditya Prakash

Computer ScienceVirginia Tech.

BSEC Conference, ORNL, Aug 26, 2015

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Networks are everywhere!

Human Disease Network [Barabasi 2007]

Gene Regulatory Network [Decourty 2008]

Facebook Network [2010]

The Internet [2005]

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Dynamical Processes over networks are also everywhere!

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Why do we care?• Social collaboration• Information Diffusion• Viral Marketing• Epidemiology and Public Health• Cyber Security• Human mobility • Games and Virtual Worlds • Ecology........

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Why do we care? (1: Epidemiology)

• Dynamical Processes over networks[AJPH 2007]

CDC data: Visualization of the first 35 tuberculosis (TB) patients and their 1039 contacts

Diseases over contact networks

SI Model

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Why do we care? (1: Epidemiology)

• Dynamical Processes over networks

• Each circle is a hospital• ~3000 hospitals• More than 30,000 patients transferred

[US-MEDICARE NETWORK 2005]

Problem: Given k units of disinfectant, whom to immunize?

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Why do we care? (1: Epidemiology)

CURRENT PRACTICE OUR METHOD

~6x fewer!

[US-MEDICARE NETWORK 2005]

Hospital-acquired inf. took 99K+ lives, cost $5B+ (all per year)

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Why do we care? (2: Online Diffusion)

> 800m users, ~$1B revenue [WSJ 2010]

~100m active users

> 50m users

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Why do we care? (2: Online Diffusion)

• Dynamical Processes over networks

Celebrity

Buy Versace™!

Followers

Social Media Marketing

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Why do we care? (3: To change the world?)

• Dynamical Processes over networks

Social networks and Collaborative Action

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High Impact – Multiple Settings

Q. How to squash rumors faster?

Q. How do opinions spread?

Q. How to market better?

epidemic out-breaks

products/viruses

transmit s/w patches

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Research Theme

DATALarge real-world

networks & processes

ANALYSISUnderstanding

POLICY/ ACTIONManaging/

Utilizing

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Research Theme – Public Health

DATAModeling # patient

transfers

ANALYSISWill an epidemic

happen?

POLICY/ ACTION

How to control out-breaks?

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Research Theme – Social Media

DATAModeling Tweets

spreading

POLICY/ ACTION

How to market better?

ANALYSIS# cascades in

future?

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In this talk

DATALarge real-world

networks & processes

Q1: How to predict Flu- trends better?

Q2: How does ‘activity’ evolve over time?

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In this talk

Q3: How to control out-breaks?

POLICY/ ACTIONUtilizing

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Outline

• Motivation• Part 1: Learning Models (Empirical Studies)• Part 2: Policy and Action (Algorithms)• Conclusion

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Part 1: Empirical Studies

• Q1: How to predict Flu-trends better?

• Q2: How does activity evolve over time?

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Surveillance• How to estimate and predict flu trends?

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Population survey

Hospital record

Lab survey

Surveillance Report

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GFT & Twitter• Estimate flu trends using online electronic

sources

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So cold today, I’m catching cold.

I have headache, sore throat, I can’t go to school today.

My nose is totally congested, I havea hard time understanding what I’msaying.

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Observation 1: States

• There are different states in an infection cycle.• SEIR model:

1. Susceptible 2. Exposed3. Infected 4. Recovered

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Observation 2: Ep. & So. Gap

• Infection cases drop exponentially in epidemiology (Hethcote 2000)

• Keyword mentions drop in a power-law pattern in social media (Matsubara 2012)

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HFSTM Model• Hidden Flu-State from Tweet Model (HFSTM)

– Each word (w) in a tweet (Oi) can be generated by:• A background topic• Non-flu related topics• State related topics

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Binary background switch

Binary non-flu related switch

Word distribution

Latent stateInitial

prob.

Transit. prob.

Transit. switch

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HFSTM Model• Generating tweets

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Generate the state for a tweetGenerate the topic for a word

State: [S,E,I] Topic: [Background,Non-flu,State]

S: goodThis restaurant is really

E: Themoviewas

goodbut it

wasfreezing

I: I think I have flu

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• EM-based algorithm: HFSTM-FIT– E-step:

• At(i)=P(O1,O2,…,Ot,St=i)

• Bt(i)=P(Ot+1,…,OTu|St=i)

• γt(i)=P(St=i|Ou)

– M-step:• Other parameters such as state transition probabilities,

topic distributions, etc.

– Parameters learned:

Inference

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A possible issue with HFSTM

• Suffers from large, noisy vocabulary. • Semi-supervision for improvement

– Introduce weak supervision into HFSTM.

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HFSTM-A

• HFSTM-A(spect)– Introduce an aspect variable y, expressing our belief on

whether a word is flu-related or not.– The value of y biases the switch variables s.t. flu-related

words are more likely to be explained by state topics.

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When the aspect value (y) is introduced, the switching probability are updated accordingly.

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Vocabulary & Dataset• Vocabulary (230 words):

– Flu-related keyword list by Chakraborty SDM 2014

– Extra state-related keyword list• Dataset (34,000 tweets):

– Identify infected users and collect their tweets– Train on data from Jun 20, 2013-Aug 06, 2013– Test on two time period:

• Dec 01, 2012- July 08, 2013• Nov 10, 2013-Jan 26, 2014

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Learned word distributions• The most probable words learned in each state

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Probably healthy: S Having symptons: E Definitely sick: I

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Learned state transitionTransition probabilities Transition in real tweets

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Not directly flu-related, yet correctly identified

Learned by HFSTM:

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Flu trend fitting

• Ground-truth: – The Pan American Health Organization (PAHO)

• Algorithms:– Baseline:

• Count the number of keywords weekly as features, and regress to the ground-truth curve.

– Google flu trend:• Take the google flu trend data as input, regress to the PAHO curve.

– HFSTM:• Distinguish different states of keyword, and only use the number

of keywords in I state. Again regress to PAHO.

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Flu trend fitting• Linear regression to the case count

reported by PAHO (the ground-truth)

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HFSTM-A

• Results are qualitatively similar with HFSTM, when the vocabulary is 10 times larger.

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See Poster!

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Part 1: Empirical Studies

• Q1: How to predict Flu-trends better?

• Q2: How does activity evolve over time?

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Google Search Volume

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

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Patterns

X

Y

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Patterns

X

Y

More Data

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Patterns

X

YAnomaly

?

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Patterns

X

YAnomaly

?

Extrapolation

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Patterns

X

YAnomalyImputation

Extrapolation

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Patterns

AnomalyImputation

Extrapolation

Compression

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• Meme (# of mentions in blogs)– short phrases Sourced from U.S. politics in 2008

“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

• 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

• Answer: YES!

• We can represent all patterns by single model

In Matsubara, Sakurai, Prakash+ SIGKDD 2012

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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)

Infectiveness of a blog-post at age n:

- Strength of infection (quality of news)

- Decay function (how infective a blog posting is)

Time n=0 Time n=nb Time n=nb+1

β

Power Law

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-1.5 slopeJ. G. Oliveira et. al. Human Dynamics: The

Correspondence Patterns of Darwin and Einstein. Nature 437, 1251 (2005) . [PDF]

(also in Leskovec, McGlohon+, SDM 2007)

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SpikeM - with periodicity

• Full equation of SpikeM

Periodicity

12pmPeak activity 3am

Low activity

Time n

Bloggers change their activity over time

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

activity

Details

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Tail-part forecasts

• SpikeM can capture tail part

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“What-if” forecasting

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

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“What-if” forecasting

–SpikeM can forecast not only tail-part, but also rise-part!

• SpikeM can forecast upcoming spikes

(1) First spike (2) Release date (3) Two weeks before release

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Modeling Malware Penetration

• Worldwide Intelligence Network– Which machine got which malware (or legitimate files)– 1 Billion nodes– 37 Billion edges

• Q: Temporal patterns?

[Papalexakakis et. al. + 2013]

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Q: Temporal Patterns

Looks familiar?

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SpikeM again (or SharkFin)

7 parameters only!

~ 400 points ~ 400 points

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Latent Propagation Patterns

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Bonus: Protest Predictions

• Can Twitter provide a lead time?• South American twitter dataset

– Language: Spanish/Portuguese– Idea

1. Look for trending keywords.2. Predict event type for protest using SpikeMparameters!

A political tweet

Violent Protest (VP)

Non Violent Protest (P)

[Sundereisan et al. ASONAM 2014][Jin et al. SIGKDD 2014]

VP

P

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Part 1: Algorithms

• Q3: How to control out-breaks?

(Broad theme: Network Topology Manipulation)

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Immunization (= Interventions)

• Different Flavors:– Pre-emptive– Data-aware

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Pre-emptive: Vulnerability• First eigenvalue λ1 (of adjacency matrix) is sufficient

for most diffusion models. [Prakash et al. ICDM’12 selected for best papers]

λ1 is the epidemic threshold

“Safe” “Vulnerable” “Deadly”

Increasing λ1 , Increasing vulnerability

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Goal

• Decrease λ1 as much as possible

• Node based [Tong, Prakash, + ICDM 2010]• Edge-based [Tong, Prakash, Eliassi-Rad+ CIKM

2012, Best Paper Award]• Edge-Manipulation (see next)

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Fractional Asymmetric Immunization

Hospital Another Hospital

Drug-resistant Bacteria (like XDR-TB)

[Prakash, Adamic, Iwashnya (M.D.) SDM 2013]

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Fractional Asymmetric Immunization

Hospital Another Hospital

Drug-resistant Bacteria (like XDR-TB)

= f

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Fractional Asymmetric Immunization

Hospital Another Hospital

Problem: Given k units of disinfectant, how to distribute them to maximize

hospitals saved?

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Our Solution

• Part 1: Value– Approximate Eigen-drop (Δ λ)– Matrix perturbation theory

• Part 2: Algorithm– Greedily pick best node at each step– Near-optimal due to submodularity

• SmartAlloc (linear complexity)

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Our Algorithm “SMART-ALLOC”

~CURRENT PRACTICE SMART-ALLOC

[US-MEDICARE NETWORK 2005]• Each circle is a hospital, ~3000 hospitals• More than 30,000 patients transferred

~6x fewer!

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Running Time

≈

Simulations (Best competitor)

SMART-ALLOC

> 1 week

14 secs

> 30,000x speed-up!

Wall-Clock Time

Lower is better

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Experiments

K = 200 K = 2000

PENN-NETWORK SECOND-LIFE

~5 x ~2.5 x

Lower is better

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Latest results

• First (provable) approximation algorithms for edge-based problem ([Saha, Adiga, Prakash, Vullikanti SDM 2015])– O(log^2 n)--factor (can be improved to O(log n))

• Based on the idea of removing closed walks

– Semi-Definite Programming Rounding-based O(1) factor

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Data-aware Immunization

Dominator tree

Graph with infected nodes

Given: Graph and Infected nodesFind: ‘best’ nodes for immunization• Complexity

– NP-hard– Hard to approximate within an absolute error

• DAVA-tree– Optimal solution on the tree

• DAVA and DAVA-fast– Merging infected nodes– Build a “dominator tree”, and run DAVA-tree

• Running time: subquadratic– DAVA: O(k(|E|+ |V|log|V|))– DAVA-fast: O(|E|+|V|log|V|)

[Zhang and Prakash, SDM 2014]

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Extensions

• Can be extended to Uncertain and noisy initial data as well!

[Zhang and Prakash, CIKM 2014]

Twitter Firehose API1% sample

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Group-based Immunization

How to select groups to minimize the epidemic?

A

FE

D

CB

• Epidemiology• Contact networks• People are grouped by ages,

demographics, occupations …

• Social Media• Friendship networks• Friends are grouped by the

same interests• E.g., Facebook pages

[Zhang, Adiga, Vullikanti, Prakash, ICDM 2015]

See Poster!

Outline

• Motivation• Part 1: Learning Models (Empirical Studies)• Part 2: Policy and Action (Algorithms)• Conclusion and Future Plans

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Future Plans

DATALarge real-world

networks & processes

ANALYSISUnderstanding

POLICY/ ACTIONManaging

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Scalability – Big Data

• Datasets of unprecedented scale– High dimensionality and sample size!

• Need scalable algorithms for – Learning Models– Developing Policy

• Leverage parallel systems– Map-Reduce clusters (like Hadoop) for data-intensive

jobs (more than 6000 machines) – Parallelized compute-intensive simulations (like Condor)

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Uncertain Data in Cascade analysis (more implementable policies)

Original, Nodes sampled off

Culprits, and missing nodes filled in

Sundereisan, Vreeken, Prakash. 2014

Correcting for missing data Designing More Robust Immunization Policies

Zhang and Prakash. CIKM 2014

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References1. Scalable Vaccine Distribution in Large Graphs given Uncertain Data (Yao Zhang and B. Aditya Prakash) -- In

CIKM 2014.2. Fast Influence-based Coarsening for Large Networks (Manish Purohit, B. Aditya Prakash, Chahhyun Kang, Yao

Zhang and V. S. Subrahmanian) – In SIGKDD 20143. DAVA: Distributing Vaccines over Large Networks under Prior Information (Yao Zhang and B. Aditya Prakash) --

In SDM 20144. Fractional Immunization on Networks (B. Aditya Prakash, Lada Adamic, Jack Iwashnya, Hanghang Tong, Christos

Faloutsos) – In SDM 20135. Spotting Culprits in Epidemics: Who and How many? (B. Aditya Prakash, Jilles Vreeken, Christos Faloutsos) – In

ICDM 2012, Brussels Vancouver (Invited to KAIS Journal Best Papers of ICDM.)6. Gelling, and Melting, Large Graphs through Edge Manipulation (Hanghang Tong, B. Aditya Prakash, Tina Eliassi-

Rad, Michalis Faloutsos, Christos Faloutsos) – In ACM CIKM 2012, Hawaii (Best Paper Award)7. Rise and Fall Patterns of Information Diffusion: Model and Implications (Yasuko Matsubara, Yasushi Sakurai, B.

Aditya Prakash, Lei Li, Christos Faloutsos) – In SIGKDD 2012, Beijing8. Interacting Viruses on a Network: Can both survive? (Alex Beutel, B. Aditya Prakash, Roni Rosenfeld, Christos

Faloutsos) – In SIGKDD 2012, Beijing9. Winner-takes-all: Competing Viruses or Ideas on fair-play networks (B. Aditya Prakash, Alex Beutel, Roni

Rosenfeld, Christos Faloutsos) – In WWW 2012, Lyon10. Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks (B. Aditya Prakash, Deepayan

Chakrabarti, Michalis Faloutsos, Nicholas Valler, Christos Faloutsos) - In IEEE ICDM 2011, Vancouver (Invited to KAIS Journal Best Papers of ICDM.)

11. Times Series Clustering: Complex is Simpler! (Lei Li, B. Aditya Prakash) - In ICML 2011, Bellevue12. Epidemic Spreading on Mobile Ad Hoc Networks: Determining the Tipping Point (Nicholas Valler, B. Aditya

Prakash, Hanghang Tong, Michalis Faloutsos and Christos Faloutsos) – In IEEE NETWORKING 2011, Valencia, Spain

13. Formalizing the BGP stability problem: patterns and a chaotic model (B. Aditya Prakash, Michalis Faloutsos and Christos Faloutsos) – In IEEE INFOCOM NetSciCom Workshop, 2011.

14. On the Vulnerability of Large Graphs (Hanghang Tong, B. Aditya Prakash, Tina Eliassi-Rad and Christos Faloutsos) – In IEEE ICDM 2010, Sydney, Australia

15. Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms (B. Aditya Prakash, Hanghang Tong, Nicholas Valler, Michalis Faloutsos and Christos Faloutsos) – In ECML-PKDD 2010, Barcelona, Spain

16. MetricForensics: A Multi-Level Approach for Mining Volatile Graphs (Keith Henderson, Tina Eliassi-Rad, Christos Faloutsos, Leman Akoglu, Lei Li, Koji Maruhashi, B. Aditya Prakash and Hanghang Tong) - In SIGKDD 2010, Washington D.C.

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Acknowledgements

Collaborators Christos Faloutsos Roni Rosenfeld, Michalis Faloutsos, Lada Adamic, Theodore Iwashyna (M.D.), Dave Andersen, Tina Eliassi-Rad, Iulian Neamtiu,

Varun Gupta, Jilles Vreeken, V. S. Subrahmanian John Brownstein (M.D.)

Deepayan Chakrabarti, Hanghang Tong, Kunal Punera, Ashwin Sridharan, Sridhar Machiraju, Mukund Seshadri, Alice Zheng, Lei Li, Polo Chau, Nicholas Valler, Alex Beutel, Xuetao Wei

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Acknowledgements

• Students Liangzhe Chen Shashidhar Sundereisan Benjamin Wang Yao Zhang Sorour Amiri

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Acknowledgements

Funding

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Analysis Policy/Action Data

Making Diffusion Work for You

B. Aditya Prakash http://www.cs.vt.edu/~badityap