influence propagation in large graphs - theorems, algorithms, and case studies

Influence propagation in large graphs - theorems,

algorithms, and case studiesChristos Faloutsos

CMU

Thank you!

• V.S. Subrahmanian• Weiru Liu• Jef Wijsen

SUM'13 C. Faloutsos (CMU) 2

C. Faloutsos (CMU) 3

Outline

• Part 1: anomaly detection– OddBall (anomaly detection)– Belief Propagation– Conclusions

• Part 2: influence propagation

SUM'13

OddBall: Spotting Anomalies in Weighted Graphs

Leman Akoglu, Mary McGlohon, Christos Faloutsos

Carnegie Mellon University School of Computer Science

PAKDD 2010, Hyderabad, India

Main idea

For each node, • extract ‘ego-net’ (=1-step-away neighbors)• Extract features (#edges, total weight, etc etc)• Compare with the rest of the population

C. Faloutsos (CMU) 5SUM'13

What is an egonet?

ego

6

egonet

C. Faloutsos (CMU)SUM'13

Selected Features Ni: number of neighbors (degree) of ego i Ei: number of edges in egonet i Wi: total weight of egonet i λw,i: principal eigenvalue of the weighted adjacency matrix of

egonet I

7C. Faloutsos (CMU)SUM'13

Near-Clique/Star

8SUM'13 C. Faloutsos (CMU)

Near-Clique/Star


Andrew Lewis (director)

Near-Clique/Star



Outline



SUM'13


E-bay Fraud detection

w/ Polo Chau &Shashank Pandit, CMU[www’07]


E-bay Fraud detection - NetProbe

Popular press

And less desirable attention:• E-mail from ‘Belgium police’ (‘copy of your

code?’)



Outline

• OddBall (anomaly detection)• Belief Propagation

– Ebay fraud– Symantec malware detection– Unification results

• Conclusions

SUM'13

Polo ChauMachine Learning Dept

Carey NachenbergVice President & Fellow

Jeffrey WilhelmPrincipal Software Engineer

Adam WrightSoftware Engineer

Prof. Christos FaloutsosComputer Science Dept

Polonium: Tera-Scale Graph Mining and Inference for Malware Detection

PATENT PENDING

SDM 2011, Mesa, Arizona

Polonium: The Data60+ terabytes of data anonymously contributed by participants of worldwide Norton Community Watch program

50+ million machines900+ million executable files

Constructed a machine-file bipartite graph (0.2 TB+)

1 billion nodes (machines and files)37 billion edges

SUM'13 20C. Faloutsos (CMU)

Polonium: Key Ideas

• Use Belief Propagation to propagate domain knowledge in machine-file graph to detect malware

• Use “guilt-by-association” (i.e., homophily)– E.g., files that appear on machines with many bad

files are more likely to be bad• Scalability: handles 37 billion-edge graph

SUM'13 21C. Faloutsos (CMU)

Polonium: One-Interaction Results

84.9% True Positive Rate1% False Positive Rate

True Positive Rate% of malware correctly identified

False Positive Rate% of non-malware wrongly labeled as malware 22

Ideal

SUM'13 C. Faloutsos (CMU)


Outline

• Part 1: anomaly detection– OddBall (anomaly detection)– Belief Propagation

• Ebay fraud• Symantec malware detection• Unification results

– Conclusions• Part 2: influence propagation

SUM'13

Unifying Guilt-by-Association Approaches:

Theorems and Fast Algorithms

Danai KoutraU Kang

Hsing-Kuo Kenneth Pao

Tai-You KeDuen Horng (Polo) Chau

Christos Faloutsos

ECML PKDD, 5-9 September 2011, Athens, Greece

Problem Definition:GBA techniques


Given: Graph; & few labeled nodesFind: labels of rest(assuming network effects)

?

?

?

?

SUM'13

Homophily and Heterophily


Step 1

Step 2

homophily heterophily

All methods handle homophily

NOT all methods handle heterophily

BUT

proposed method does!

SUM'13

Are they related?

• RWR (Random Walk with Restarts) – google’s pageRank (‘if my friends are important,

I’m important, too’)• SSL (Semi-supervised learning)

– minimize the differences among neighbors• BP (Belief propagation)

– send messages to neighbors, on what you believe about them


Are they related?

• RWR (Random Walk with Restarts) – google’s pageRank (‘if my friends are important,

I’m important, too’)• SSL (Semi-supervised learning)

– minimize the differences among neighbors• BP (Belief propagation)

– send messages to neighbors, on what you believe about them


YES!

Correspondence of Methods


Method Matrix Unknown knownRWR [I – c AD-1] × x = (1-c)ySSL [I + a(D - A)] × x = y

FABP [I + a D - c’A] × bh = φh

0 1 01 0 10 1 0

? 0 1 1

1 1 1

d1 d2 d3

final labels/ beliefs

prior labels/ beliefs

adjacency matrix

SUM'13

Results: Scalability


FABP is linear on the number of edges.

# of edges (Kronecker graphs)

runti

me

(min

)

SUM'13

Results (5): Parallelism


FABP ~2x faster & wins/ties on accuracy.

runtime (min)

% a

ccur

acy

SUM'13


Conclusions

• Anomaly detection: hand-in-hand with pattern discovery (‘anomalies’ == ‘rare patterns’)

• ‘OddBall’ for large graphs

• ‘NetProbe’ and belief propagation: exploit network effects.

• FaBP: fast & accurate

SUM'13


Outline



SUM'13

Influence propagation in large graphs -

theorems and algorithmsB. Aditya Prakash

http://www.cs.cmu.edu/~badityap

Christos Faloutsoshttp://www.cs.cmu.edu/~christos

Carnegie Mellon University

Networks are everywhere!

Human Disease Network [Barabasi 2007]

Gene Regulatory Network [Decourty 2008]

Facebook Network [2010]

The Internet [2005]


Dynamical Processes over networks are also everywhere!


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


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



• 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?



CURRENT PRACTICE OUR METHOD

~6x fewer!

[US-MEDICARE NETWORK 2005]

C. Faloutsos (CMU) 40SUM'13Hospital-acquired inf. took 99K+ lives, cost $5B+ (all per year)

Why do we care? (2: Online Diffusion)

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

~100m active users

> 50m users


Why do we care? (2: Online Diffusion)

• Dynamical Processes over networks

Celebrity

Buy Versace™!

Followers

Social Media MarketingC. Faloutsos (CMU) 42SUM'13

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


Research Theme

DATALarge real-world

networks & processes

ANALYSISUnderstanding

POLICY/ ACTIONManaging


In this talk

ANALYSISUnderstanding

Given propagation models:

Q1: Will an epidemic happen?


In this talk

Q2: How to immunize and control out-breaks better?

POLICY/ ACTIONManaging


Outline

• Part 1: anomaly detection• Part 2: influence propagation

• Motivation• Epidemics: what happens? (Theory)• Action: Who to immunize? (Algorithms)


A fundamental questionStrong Virus

Epidemic?


example (static graph)Weak Virus

Epidemic?


Problem Statement

Find, a condition under which– virus will die out exponentially quickly– regardless of initial infection condition

above (epidemic)

below (extinction)

# Infected

time

Separate the regimes?


Threshold (static version)

Problem Statement• Given:

–Graph G, and –Virus specs (attack prob. etc.)

• Find: –A condition for virus extinction/invasion


Threshold: Why important?

• Accelerating simulations• Forecasting (‘What-if’ scenarios)• Design of contagion and/or topology• A great handle to manipulate the spreading

– Immunization– Maximize collaboration…..


Outline

• Motivation• Epidemics: what happens? (Theory)

– Background– Result (Static Graphs)– Proof Ideas (Static Graphs)– Bonus 1: Dynamic Graphs– Bonus 2: Competing Viruses

• Action: Who to immunize? (Algorithms)


“SIR” model: life immunity (mumps)

• Each node in the graph is in one of three states– Susceptible (i.e. healthy)– Infected– Removed (i.e. can’t get infected again)

Prob. β Prob. δ

t = 1 t = 2 t = 3

Background


Terminology: continued

• Other virus propagation models (“VPM”)– SIS : susceptible-infected-susceptible, flu-like– SIRS : temporary immunity, like pertussis– SEIR : mumps-like, with virus incubation (E = Exposed)….………….

• Underlying contact-network – ‘who-can-infect-whom’

Background


Related Work R. M. Anderson and R. M. May. Infectious Diseases of Humans. Oxford University Press,

1991. A. Barrat, M. Barthélemy, and A. Vespignani. Dynamical Processes on Complex Networks.

Cambridge University Press, 2010. F. M. Bass. A new product growth for model consumer durables. Management Science,

15(5):215–227, 1969. D. Chakrabarti, Y. Wang, C. Wang, J. Leskovec, and C. Faloutsos. Epidemic thresholds in

real networks. ACM TISSEC, 10(4), 2008. D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly

Connected World. Cambridge University Press, 2010. A. Ganesh, L. Massoulie, and D. Towsley. The effect of network topology in spread of

epidemics. IEEE INFOCOM, 2005. Y. Hayashi, M. Minoura, and J. Matsukubo. Recoverable prevalence in growing scale-free

networks and the effective immunization. arXiv:cond-at/0305549 v2, Aug. 6 2003. H. W. Hethcote. The mathematics of infectious diseases. SIAM Review, 42, 2000. H. W. Hethcote and J. A. Yorke. Gonorrhea transmission dynamics and control. Springer

Lecture Notes in Biomathematics, 46, 1984. J. O. Kephart and S. R. White. Directed-graph epidemiological models of computer

viruses. IEEE Computer Society Symposium on Research in Security and Privacy, 1991. J. O. Kephart and S. R. White. Measuring and modeling computer virus prevalence. IEEE

Computer Society Symposium on Research in Security and Privacy, 1993. R. Pastor-Santorras and A. Vespignani. Epidemic spreading in scale-free networks.

Physical Review Letters 86, 14, 2001.

……… ……… ………

All are about either:

• Structured topologies (cliques, block-diagonals, hierarchies, random)

• Specific virus propagation models

• Static graphs

Background


Outline





How should the answer look like?

• Answer should depend on:– Graph– Virus Propagation Model (VPM)

• But how??– Graph – average degree? max. degree? diameter?– VPM – which parameters? – How to combine – linear? quadratic? exponential?

?diameterdavg ?/)( max22 ddd avgavg …..


Static Graphs: Our Main Result

• Informally,

•

For, any arbitrary topology (adjacency matrix A) any virus propagation model (VPM) in standard literature

the epidemic threshold depends only 1. on the λ, first eigenvalue of A, and 2. some constant , determined by

the virus propagation model

λVPMC

No epidemic if λ *

< 1

VPMCVPMC

C. Faloutsos (CMU) 59SUM'13In Prakash+ ICDM 2011 (Selected among best papers).

w/ DeepayChakrabarti

Our thresholds for some models

• s = effective strength• s < 1 : below threshold

Models Effective Strength (s) Threshold (tipping point)

SIS, SIR, SIRS, SEIRs = λ .

s = 1

SIV, SEIV s = λ .

(H.I.V.) s = λ .

12

221

vv

v

2121 VVISIC. Faloutsos (CMU) 60SUM'13

Our result: Intuition for λ

“Official” definition:• Let A be the adjacency

matrix. Then λ is the root with the largest magnitude of the characteristic polynomial of A [det(A – xI)].

• Doesn’t give much intuition!

“Un-official” Intuition • λ ~ # paths in the

graph

uu≈ .

kkA

(i, j) = # of paths i j of length k

kA


Largest Eigenvalue (λ)

λ ≈ 2 λ = N λ = N-1

N = 1000λ ≈ 2 λ= 31.67 λ= 999

better connectivity higher λ

C. Faloutsos (CMU) 62SUM'13 N nodes

Examples: Simulations – SIR (mumps)

(a) Infection profile (b) “Take-off” plot

PORTLAND graph: synthetic population, 31 million links, 6 million nodes

Frac

tion

of In

fecti

ons

Foot

prin

tEffective StrengthTime ticks


Examples: Simulations – SIRS (pertusis)

Frac

tion

of In

fecti

ons

Foot

prin

tEffective StrengthTime ticks

(a) Infection profile (b) “Take-off” plot

PORTLAND graph: synthetic population, 31 million links, 6 million nodesC. Faloutsos (CMU) 64SUM'13

λ * < 1

VPMC

Graph-based

Model-based

65

General VPM structure

Topology and stability

See paper for full proof

SUM'13 C. Faloutsos (CMU)

Outline





λ * < 1VPMC

Graph-based

Model-basedGeneral VPM structure

Topology and stability

See paper for full proof


Outline





Dynamic Graphs: Epidemic?

adjacency matrix

8

8

Alternating behaviorsDAY (e.g., work)


adjacency matrix

8

8

Dynamic Graphs: Epidemic?Alternating behaviorsNIGHT

(e.g., home)


• SIS model– recovery rate δ– infection rate β

• Set of T arbitrary graphs

Model Description

day

N

N night

N

N , weekend…..

Infected

Healthy

XN1

N3

N2

Prob. βProb. β Prob. δ


• Informally, NO epidemic if

eig (S) = < 1

Our result: Dynamic Graphs Threshold

Single number! Largest eigenvalue of The system matrix S

In Prakash+, ECML-PKDD 2010

S =

Details


Synthetic MIT Reality Mining

log(fraction infected)

Time

BELOW

AT

ABOVE ABOVE

AT

BELOW

Infection-profile


“Take-off” plotsFootprint (# infected @ “steady state”)

Our threshold

Our threshold

(log scale)

NO EPIDEMIC

EPIDEMIC

EPIDEMIC

NO EPIDEMIC

Synthetic MIT Reality


Outline





Competing Contagions

iPhone v Android Blu-ray v HD-DVD

76SUM'13 C. Faloutsos (CMU)Biological common flu/avian flu, pneumococcal inf etc

A simple model

• Modified flu-like • Mutual Immunity (“pick one of the two”)• Susceptible-Infected1-Infected2-Susceptible

Virus 1 Virus 2

Details


Question: What happens in the end?

green: virus 1red: virus 2

Footprint @ Steady State Footprint @ Steady State= ?

Number of Infections


ASSUME: Virus 1 is stronger than Virus 2

Question: What happens in the end?



Strength Strength

??= Strength Strength

2

Footprint @ Steady State Footprint @ Steady State



Answer: Winner-Takes-All





Our Result: Winner-Takes-All

Given our model, and any graph, the weaker virus always dies-out completely

1. The stronger survives only if it is above threshold 2. Virus 1 is stronger than Virus 2, if: strength(Virus 1) > strength(Virus 2)3. Strength(Virus) = λ β / δ same as before!

Details

81SUM'13 C. Faloutsos (CMU)In Prakash, Beutel, + WWW 2012

Real Examples

Reddit v Digg Blu-Ray v HD-DVD

[Google Search Trends data]


Outline



?

?

Given: a graph A, virus prop. model and budget k; Find: k ‘best’ nodes for immunization (removal).

k = 2

??

Full Static Immunization


Outline


– Full Immunization (Static Graphs)– Fractional Immunization


Challenges

• Given a graph A, budget k, Q1 (Metric) How to measure the ‘shield-

value’ for a set of nodes (S)?

Q2 (Algorithm) How to find a set of k nodes with highest ‘shield-value’?


Proposed vulnerability measure λ

Increasing λ Increasing vulnerability

λ is the epidemic threshold

“Safe” “Vulnerable” “Deadly”


1

9

10

3

4

5

7

8

6

2

9

1

11

10

3

4

56

7

8

2

9

Original Graph Without {2, 6}

Eigen-Drop(S) Δ λ = λ - λs

Δ

A1: “Eigen-Drop”: an ideal shield value


(Q2) - Direct Algorithm too expensive!

• Immunize k nodes which maximize Δ λ

S = argmax Δ λ• Combinatorial!• Complexity:

– Example: • 1,000 nodes, with 10,000 edges • It takes 0.01 seconds to compute λ• It takes 2,615 years to find 5-best nodes!


A2: Our Solution

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

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

• NetShield (linear complexity)– O(nk2+m) n = # nodes; m = # edges

C. Faloutsos (CMU) 90SUM'13In Tong, Prakash+ ICDM 2010

Experiment: Immunization quality

Log(fraction of infected nodes)

NetShield

Degree

PageRank

Eigs (=HITS)Acquaintance

Betweeness (shortest path)

Lower is

better TimeC. Faloutsos (CMU) 91SUM'13

Outline


– Full Immunization (Static Graphs)– Fractional Immunization


Fractional Immunization of NetworksB. Aditya Prakash, Lada Adamic, Theodore Iwashyna (M.D.), Hanghang Tong, Christos Faloutsos

Under review


Fractional Asymmetric Immunization

Hospital Another Hospital

Drug-resistant Bacteria (like XDR-TB)




Drug-resistant Bacteria (like XDR-TB)




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

hospitals saved?


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!


Running Time

≈

Simulations SMART-ALLOC

> 1 week

14 secs

> 30,000x speed-up!

Wall-Clock Time

Lower is better


Experiments

K = 200 K = 2000

PENN-NETWORK SECOND-LIFE

~5 x ~2.5 x

Lower is better


Acknowledgements

Funding


References1. 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.)

2. 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

3. 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

4. Winner-takes-all: Competing Viruses or Ideas on fair-play networks (B. Aditya Prakash, Alex Beutel, Roni Rosenfeld, Christos Faloutsos) – In WWW 2012, Lyon

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

6. Fractional Immunization of Networks (B. Aditya Prakash, Lada Adamic, Theodore Iwashyna, Hanghang Tong, Christos Faloutsos) - Under Submission

7. Rise and Fall Patterns of Information Diffusion: Model and Implications (Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, Christos Faloutsos) - Under Submission

101

http://www.cs.vt.edu/~badityap/SUM'13 C. Faloutsos (CMU)

Analysis Policy/Action Data

Propagation on Large Networks

B. Aditya Prakash Christos Faloutsos


influence propagation in large graphs - theorems, algorithms, and case studies

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