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Transcript of Influence propagation in large graphs - theorems, algorithms, and case studies Christos Faloutsos...
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
9C. Faloutsos (CMU)SUM'13
Near-Clique/Star
10C. Faloutsos (CMU)SUM'13
Andrew Lewis (director)
Near-Clique/Star
11C. Faloutsos (CMU)SUM'13
C. Faloutsos (CMU) 12
Outline
• Part 1: anomaly detection– OddBall (anomaly detection)– Belief Propagation– Conclusions
• Part 2: influence propagation
SUM'13
SUM'13 C. Faloutsos (CMU) 13
E-bay Fraud detection
w/ Polo Chau &Shashank Pandit, CMU[www’07]
SUM'13 C. Faloutsos (CMU) 14
E-bay Fraud detection
SUM'13 C. Faloutsos (CMU) 15
E-bay Fraud detection
SUM'13 C. Faloutsos (CMU) 16
E-bay Fraud detection - NetProbe
Popular press
And less desirable attention:• E-mail from ‘Belgium police’ (‘copy of your
code?’)
SUM'13 C. Faloutsos (CMU) 17
C. Faloutsos (CMU) 18
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)
C. Faloutsos (CMU) 23
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
C. Faloutsos (CMU) 25
Given: Graph; & few labeled nodesFind: labels of rest(assuming network effects)
?
?
?
?
SUM'13
Homophily and Heterophily
C. Faloutsos (CMU) 26
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
SUM'13 C. Faloutsos (CMU) 27
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
SUM'13 C. Faloutsos (CMU) 28
YES!
Correspondence of Methods
C. Faloutsos (CMU) 29
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
C. Faloutsos (CMU) 30
FABP is linear on the number of edges.
# of edges (Kronecker graphs)
runti
me
(min
)
SUM'13
Results (5): Parallelism
C. Faloutsos (CMU) 31
FABP ~2x faster & wins/ties on accuracy.
runtime (min)
% a
ccur
acy
SUM'13
C. Faloutsos (CMU) 32
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
C. Faloutsos (CMU) 33
Outline
• Part 1: anomaly detection– OddBall (anomaly detection)– Belief Propagation– Conclusions
• Part 2: influence propagation
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]
C. Faloutsos (CMU) 35SUM'13
Dynamical Processes over networks are also everywhere!
C. Faloutsos (CMU) 36SUM'13
Why do we care?• Information Diffusion• Viral Marketing• Epidemiology and Public Health• Cyber Security• Human mobility • Games and Virtual Worlds • Ecology• Social Collaboration........
C. Faloutsos (CMU) 37SUM'13
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
C. Faloutsos (CMU) 38SUM'13
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?
C. Faloutsos (CMU) 39SUM'13
Why do we care? (1: Epidemiology)
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
C. Faloutsos (CMU) 41SUM'13
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
C. Faloutsos (CMU) 43SUM'13
Research Theme
DATALarge real-world
networks & processes
ANALYSISUnderstanding
POLICY/ ACTIONManaging
C. Faloutsos (CMU) 44SUM'13
In this talk
ANALYSISUnderstanding
Given propagation models:
Q1: Will an epidemic happen?
C. Faloutsos (CMU) 45SUM'13
In this talk
Q2: How to immunize and control out-breaks better?
POLICY/ ACTIONManaging
C. Faloutsos (CMU) 46SUM'13
Outline
• Part 1: anomaly detection• Part 2: influence propagation
• Motivation• Epidemics: what happens? (Theory)• Action: Who to immunize? (Algorithms)
C. Faloutsos (CMU) 47SUM'13
A fundamental questionStrong Virus
Epidemic?
C. Faloutsos (CMU) 48SUM'13
example (static graph)Weak Virus
Epidemic?
C. Faloutsos (CMU) 49SUM'13
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?
C. Faloutsos (CMU) 50SUM'13
Threshold (static version)
Problem Statement• Given:
–Graph G, and –Virus specs (attack prob. etc.)
• Find: –A condition for virus extinction/invasion
C. Faloutsos (CMU) 51SUM'13
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…..
C. Faloutsos (CMU) 52SUM'13
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)
C. Faloutsos (CMU) 53SUM'13
“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
C. Faloutsos (CMU) 54SUM'13
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
C. Faloutsos (CMU) 55SUM'13
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
C. Faloutsos (CMU) 56SUM'13
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)
C. Faloutsos (CMU) 57SUM'13
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 …..
C. Faloutsos (CMU) 58SUM'13
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
C. Faloutsos (CMU) 61SUM'13
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
C. Faloutsos (CMU) 63SUM'13
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
• 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)
C. Faloutsos (CMU) 66SUM'13
λ * < 1VPMC
Graph-based
Model-basedGeneral VPM structure
Topology and stability
See paper for full proof
67SUM'13 C. Faloutsos (CMU)
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)
C. Faloutsos (CMU) 68SUM'13
Dynamic Graphs: Epidemic?
adjacency matrix
8
8
Alternating behaviorsDAY (e.g., work)
C. Faloutsos (CMU) 69SUM'13
adjacency matrix
8
8
Dynamic Graphs: Epidemic?Alternating behaviorsNIGHT
(e.g., home)
C. Faloutsos (CMU) 70SUM'13
• 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
C. Faloutsos (CMU) 71SUM'13
• 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
C. Faloutsos (CMU) 72SUM'13
Synthetic MIT Reality Mining
log(fraction infected)
Time
BELOW
AT
ABOVE ABOVE
AT
BELOW
Infection-profile
C. Faloutsos (CMU) 73SUM'13
“Take-off” plotsFootprint (# infected @ “steady state”)
Our threshold
Our threshold
(log scale)
NO EPIDEMIC
EPIDEMIC
EPIDEMIC
NO EPIDEMIC
Synthetic MIT Reality
C. Faloutsos (CMU) 74SUM'13
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)
C. Faloutsos (CMU) 75SUM'13
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
C. Faloutsos (CMU) 77SUM'13
Question: What happens in the end?
green: virus 1red: virus 2
Footprint @ Steady State Footprint @ Steady State= ?
Number of Infections
C. Faloutsos (CMU) 78SUM'13
ASSUME: Virus 1 is stronger than Virus 2
Question: What happens in the end?
green: virus 1red: virus 2
Number of Infections
Strength Strength
??= Strength Strength
2
Footprint @ Steady State Footprint @ Steady State
79SUM'13 C. Faloutsos (CMU)
ASSUME: Virus 1 is stronger than Virus 2
Answer: Winner-Takes-All
green: virus 1red: virus 2
Number of Infections
80SUM'13 C. Faloutsos (CMU)
ASSUME: Virus 1 is stronger than Virus 2
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]
82SUM'13 C. Faloutsos (CMU)
Outline
• Motivation• Epidemics: what happens? (Theory)• Action: Who to immunize? (Algorithms)
C. Faloutsos (CMU) 83SUM'13
?
?
Given: a graph A, virus prop. model and budget k; Find: k ‘best’ nodes for immunization (removal).
k = 2
??
Full Static Immunization
C. Faloutsos (CMU) 84SUM'13
Outline
• Motivation• Epidemics: what happens? (Theory)• Action: Who to immunize? (Algorithms)
– Full Immunization (Static Graphs)– Fractional Immunization
C. Faloutsos (CMU) 85SUM'13
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’?
C. Faloutsos (CMU) 86SUM'13
Proposed vulnerability measure λ
Increasing λ Increasing vulnerability
λ is the epidemic threshold
“Safe” “Vulnerable” “Deadly”
C. Faloutsos (CMU) 87SUM'13
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
C. Faloutsos (CMU) 88SUM'13
(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!
C. Faloutsos (CMU) 89SUM'13
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
• Motivation• Epidemics: what happens? (Theory)• Action: Who to immunize? (Algorithms)
– Full Immunization (Static Graphs)– Fractional Immunization
C. Faloutsos (CMU) 92SUM'13
Fractional Immunization of NetworksB. Aditya Prakash, Lada Adamic, Theodore Iwashyna (M.D.), Hanghang Tong, Christos Faloutsos
Under review
C. Faloutsos (CMU) 93SUM'13
Fractional Asymmetric Immunization
Hospital Another Hospital
Drug-resistant Bacteria (like XDR-TB)
C. Faloutsos (CMU) 94SUM'13
Fractional Asymmetric Immunization
Hospital Another Hospital
Drug-resistant Bacteria (like XDR-TB)
C. Faloutsos (CMU) 95SUM'13
Fractional Asymmetric Immunization
Hospital Another Hospital
Problem: Given k units of disinfectant, how to distribute them to maximize
hospitals saved?
C. Faloutsos (CMU) 96SUM'13
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!
C. Faloutsos (CMU) 97SUM'13
Running Time
≈
Simulations SMART-ALLOC
> 1 week
14 secs
> 30,000x speed-up!
Wall-Clock Time
Lower is better
C. Faloutsos (CMU) 98SUM'13
Experiments
K = 200 K = 2000
PENN-NETWORK SECOND-LIFE
~5 x ~2.5 x
Lower is better
C. Faloutsos (CMU) 99SUM'13
Acknowledgements
Funding
C. Faloutsos (CMU) 100SUM'13
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
102SUM'13 C. Faloutsos (CMU)