Download presentation

Presentation is loading. Please wait.

Published bySamuel Milford Modified over 3 years ago

1
**Understanding and Managing Cascades on Large Graphs**

B. Aditya Prakash Carnegie Mellon University Virginia Tech. Christos Faloutsos Carnegie Mellon University Aug 29, Tutorial, VLDB 2012, Istanbul

2
**Networks are everywhere!**

Facebook Network [2010] Gene Regulatory Network [Decourty 2008] Human Disease Network [Barabasi 2007] The Internet [2005] Prakash and Faloutsos 2012

3
**Dynamical Processes over networks are also everywhere!**

Prakash and Faloutsos 2012

4
**Why do we care? Social collaboration Information Diffusion**

Viral Marketing Epidemiology and Public Health Cyber Security Human mobility Games and Virtual Worlds Ecology Prakash and Faloutsos 2012

5
**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 Prakash and Faloutsos 2012

6
**Why do we care? (1: Epidemiology)**

Dynamical Processes over networks Each circle is a hospital ~3000 hospitals More than 30,000 patients transferred Mention number of hospitals Patients transferred [US-MEDICARE NETWORK 2005] Problem: Given k units of disinfectant, whom to immunize? Prakash and Faloutsos 2012

7
**Why do we care? (1: Epidemiology)**

~6x fewer! [US-MEDICARE NETWORK 2005] CURRENT PRACTICE OUR METHOD Hospital-acquired inf. took 99K+ lives, cost $5B+ (all per year) Prakash and Faloutsos 2012

8
**Why do we care? (2: Online Diffusion)**

> 800m users, ~$1B revenue [WSJ 2010] ~100m active users > 50m users Prakash and Faloutsos 2012

9
**Why do we care? (2: Online Diffusion)**

Dynamical Processes over networks Buy Versace™! Celebrity Followers Social Media Marketing Prakash and Faloutsos 2012

10
**Why do we care? (3: To change the world?)**

Dynamical Processes over networks Social networks and Collaborative Action Prakash and Faloutsos 2012

11
**High Impact – Multiple Settings**

epidemic out-breaks Q. How to squash rumors faster? Q. How do opinions spread? Q. How to market better? products/viruses transmit s/w patches Prakash and Faloutsos 2012

12
**Large real-world networks & processes**

Research Theme ANALYSIS Understanding POLICY/ ACTION Managing DATA Large real-world networks & processes Prakash and Faloutsos 2012

13
**Research Theme – Public Health**

ANALYSIS Will an epidemic happen? POLICY/ ACTION How to control out-breaks? DATA Modeling # patient transfers Prakash and Faloutsos 2012

14
**Research Theme – Social Media**

ANALYSIS # cascades in future? POLICY/ ACTION How to market better? DATA Modeling Tweets spreading Prakash and Faloutsos 2012

15
**In this tutorial Given propagation models: Q1: What is the epidemic**

ANALYSIS Understanding Given propagation models: Q1: What is the epidemic threshold? Q2: How do viruses compete? Prakash and Faloutsos 2012

16
**In this tutorial Q3: How to immunize and control out-breaks better?**

POLICY/ ACTION Managing Q3: How to immunize and control out-breaks better? Q4: How to detect outbreaks? Q5: Who are the culprits? Prakash and Faloutsos 2012

17
**Large real-world networks & processes**

In this tutorial DATA Large real-world networks & processes Q6: How do cascades look like? Q7: How does activity evolve over time? Q8: How does external influence act? Prakash and Faloutsos 2012

18
**Outline Motivation Part 1: Understanding Epidemics (Theory)**

Part 2: Policy and Action (Algorithms) Part 3: Learning Models (Empirical Studies) Conclusion single virus VS multiple viruses Prakash and Faloutsos 2012

19
**Part 1: Theory Q1: What is the epidemic threshold?**

Q2: How do viruses compete? Prakash and Faloutsos 2012

20
**A fundamental question**

Strong Virus Epidemic? Prakash and Faloutsos 2012

21
**example (static graph)**

Weak Virus Epidemic? Prakash and Faloutsos 2012

22
**Problem Statement Find, a condition under which # Infected**

above (epidemic) below (extinction) # Infected time Find, a condition under which virus will die out exponentially quickly regardless of initial infection condition Separate the regimes? Prakash and Faloutsos 2012

23
**Threshold (static version)**

Problem Statement Given: Graph G, and Virus specs (attack prob. etc.) Find: A condition for virus extinction/invasion Prakash and Faloutsos 2012

24
**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 ….. Bring the snapshot of previous slide Prakash and Faloutsos 2012

25
**Part 1: Theory Q1: What is the epidemic threshold?**

Background Result and Intuition (Static Graphs) Proof Ideas (Static Graphs) Bonus: Dynamic Graphs Q2: How do viruses compete? Prakash and Faloutsos 2012

26
**“SIR” model: life immunity (mumps)**

Background “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. δ Prob. β spend less time explaining the stuff…. t = 1 t = 2 t = 3 Prakash and Faloutsos 2012

27
**Terminology: continued**

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’ Prakash and Faloutsos 2012

28
Background Related Work All are about either: Structured topologies (cliques, block-diagonals, hierarchies, random) Specific virus propagation models Static graphs 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/ v2, Aug 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. ……… Point out may anderson is a book, survey Prakash and Faloutsos 2012

29
**Part 1: Theory Q1: What is the epidemic threshold?**

Background Result and Intuition (Static Graphs) Proof Ideas (Static Graphs) Bonus: Dynamic Graphs Q2: How do viruses compete? Prakash and Faloutsos 2012

30
**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? Put example equations (\beta d + \delta/diameter) \beta\sqrt(s) ….. Prakash and Faloutsos 2012

31
**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 on the λ, ﬁrst eigenvalue of A, and some constant , determined by the virus propagation model λ No epidemic if λ * < 1 In Prakash+ ICDM 2011 Prakash and Faloutsos 2012

32
**Our thresholds for some models**

s = effective strength s < 1 : below threshold Models Effective Strength (s) Threshold (tipping point) SIS, SIR, SIRS, SEIR s = λ . s = 1 SIV, SEIV (H.I.V.) Prakash and Faloutsos 2012

33
**Our result: Intuition for λ**

“Official” definition: “Un-official” Intuition 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! λ ~ # paths in the graph u u ≈ . Have a better intuition slide ‘unofficial intuition’ (i, j) = # of paths i j of length k Prakash and Faloutsos 2012

34
**Largest Eigenvalue (λ)**

better connectivity higher λ λ ≈ 2 λ = N λ = N-1 Square root λ ≈ 2 λ= 31.67 λ= 999 N = 1000 N nodes Prakash and Faloutsos 2012

35
**Examples: Simulations – SIR (mumps)**

Fraction of Infections Footprint Cite Barrett and Marathe (a) Infection profile (b) “Take-off” plot PORTLAND graph 31 million links, 6 million nodes Effective Strength Time ticks Prakash and Faloutsos 2012

36
**Examples: Simulations – SIRS (pertusis)**

Fraction of Infections Footprint (a) Infection profile (b) “Take-off” plot PORTLAND graph 31 million links, 6 million nodes Time ticks Effective Strength Prakash and Faloutsos 2012

37
**Part 1: Theory Q1: What is the epidemic threshold?**

Background Result and Intuition (Static Graphs) Proof Ideas (Static Graphs) Bonus: Dynamic Graphs Q2: How do viruses compete? Prakash and Faloutsos 2012

38
**Proof Sketch λ * < 1 General VPM structure Model-based Graph-based**

λ * < 1 Dimensional arguments… Full proof 30 pages on arxiv Graph-based Topology and stability Prakash and Faloutsos 2012

39
**Models and more models H.I.V. Model Used for SIR Mumps SIS Flu SIRS**

Pertussis SEIR Chicken-pox …….. SICR Tuberculosis MSIR Measles SIV Sensor Stability H.I.V. ………. Prakash and Faloutsos 2012

40
**Ingredient 1: Our generalized model**

Exogenous Transitions Endogenous Transitions Susceptible Infected Vigilant Endogenous Transitions Susceptible Infected Vigilant (S*I*V*?) Prakash and Faloutsos 2012

41
**Special case: SIR Susceptible Infected Vigilant (S*I*V*?)**

Prakash and Faloutsos 2012

42
**Special case: H.I.V. Multiple Infectious, Vigilant states**

“Non-terminal” “Terminal” Multiple Infectious, Vigilant states Prakash and Faloutsos 2012

43
**Ingredient 2: NLDS+Stability**

Details Ingredient 2: NLDS+Stability View as a NLDS discrete time non-linear dynamical system (NLDS) size mN x 1 . size N (number of nodes in the graph) S Probability vector Specifies the state of the system at time t Curly braces…say what the blue yellow are… I V Prakash and Faloutsos 2012

44
**Ingredient 2: NLDS + Stability**

Details Ingredient 2: NLDS + Stability View as a NLDS discrete time non-linear dynamical system (NLDS) size mN x 1 . Non-linear function Explicitly gives the evolution of system Prakash and Faloutsos 2012

45
**Ingredient 2: NLDS + Stability**

View as a NLDS discrete time non-linear dynamical system (NLDS) Threshold Stability of NLDS Prakash and Faloutsos 2012

46
**Special case: SIR Details NLDS size 3N x 1 = probability that node**

Apply g on p_T, you get p_t+1 = probability that node i is not attacked by any of its infectious neighbors NLDS Prakash and Faloutsos 2012

47
**Fixed Point Details State when no node is infected Q: Is it stable? 1**

. State when no node is infected Q: Is it stable? Prakash and Faloutsos 2012

48
**Stability for SIR Stable under threshold Unstable above threshold**

Prakash and Faloutsos 2012

49
**λ * < 1 See paper for full proof General VPM structure Model-based**

λ * < 1 Dimensional arguments… Graph-based Topology and stability Prakash and Faloutsos 2012

50
**Part 1: Theory Q1: What is the epidemic threshold?**

Background Result and Intuition (Static Graphs) Proof Ideas (Static Graphs) Bonus: Dynamic Graphs Q2: How do viruses compete? Prakash and Faloutsos 2012

51
**Dynamic Graphs: Epidemic?**

Alternating behaviors DAY (e.g., work) adjacency matrix 8 Prakash and Faloutsos 2012

52
**Dynamic Graphs: Epidemic?**

Alternating behaviors NIGHT (e.g., home) adjacency matrix 8 Prakash and Faloutsos 2012

53
**Model Description SIS model Set of T arbitrary graphs recovery rate δ**

Infected Healthy X N1 N3 N2 Prob. β Prob. δ SIS model recovery rate δ infection rate β Set of T arbitrary graphs weekend day N night N , weekend….. Prakash and Faloutsos 2012

54
**Our result: Dynamic Graphs Threshold**

Informally, NO epidemic if eig (S) = < 1 Single number! Largest eigenvalue of The system matrix S Details S = In Prakash+, ECML-PKDD 2010 Prakash and Faloutsos 2012

55
**Infection-profile Synthetic MIT Reality Mining log(fraction infected)**

ABOVE ABOVE AT AT write ‘below threshold’ for eig < 1 AT threshold above threshold BELOW BELOW Time Prakash and Faloutsos 2012

56
**“Take-off” plots Synthetic MIT Reality Our threshold Our threshold**

Footprint (# “steady state”) Synthetic MIT Reality EPIDEMIC Our threshold Our threshold EPIDEMIC Plot variance instead? Size and NO EPIDEMIC NO EPIDEMIC (log scale) Prakash and Faloutsos 2012

57
**Part 1: Theory Q1: What is the epidemic threshold?**

Q2: What happens when viruses compete? Mutually-exclusive viruses Interacting viruses Prakash and Faloutsos 2012

58
**Competing Contagions iPhone v Android Blu-ray v HD-DVD v Attack**

Retreat Biological common flu/avian flu, pneumococcal inf etc Prakash and Faloutsos 2012

59
**A simple model Virus 2 Virus 1 Details Modified flu-like**

Mutual Immunity (“pick one of the two”) Susceptible-Infected1-Infected2-Susceptible Android apple sign for the viruses Virus 1 Virus 2 Prakash and Faloutsos 2012

60
**Question: What happens in the end?**

green: virus 1 red: virus 2 Number of Infections Steady State = ? Steady-state number of greens/num of reds = ? ASSUME: Virus 1 is stronger than Virus 2 Prakash and Faloutsos 2012

61
**Question: What happens in the end?**

Steady State green: virus 1 red: virus 2 Number of Infections Strength ?? = 2 Strength Steady-state number of greens/num of reds = ? ASSUME: Virus 1 is stronger than Virus 2 Prakash and Faloutsos 2012

62
**Answer: Winner-Takes-All**

green: virus 1 red: virus 2 Number of Infections ASSUME: Virus 1 is stronger than Virus 2 Prakash and Faloutsos 2012

63
**Our Result: Winner-Takes-All**

Given our model, and any graph, the weaker virus always dies-out completely Details The stronger survives only if it is above threshold Virus 1 is stronger than Virus 2, if: strength(Virus 1) > strength(Virus 2) Strength(Virus) = λ β / δ same as before! In Prakash+ WWW 2012 Prakash and Faloutsos 2012

64
**Real Examples [Google Search Trends data] Reddit v Digg**

logos Reddit v Digg Blu-Ray v HD-DVD Prakash and Faloutsos 2012

65
**Part 1: Theory Q1: What is the epidemic threshold?**

Q2: What happens when viruses compete? Mutually-exclusive viruses Interacting viruses Prakash and Faloutsos 2012

66
**A simple model: SI1|2S & Virus 1 Virus 2 Modified flu-like (SIS)**

Susceptible-Infected1 or 2-Susceptible Interaction Factor ε Full Mutual Immunity: ε = 0 Partial Mutual Immunity (competition): ε < 0 Cooperation: ε > 0 & Virus 1 Virus 2 Prakash and Faloutsos 2012

67
**Question: What happens in the end?**

ε = 0 Winner takes all ε = 1 Co-exist independently ε = 2 Viruses cooperate Strengths: 6 and 4 What about for 0 < ε <1? Is there a point at which both viruses can co-exist? ASSUME: Virus 1 is stronger than Virus 2 Prakash and Faloutsos 2012

68
**Answer: Yes! There is a phase transition**

ASSUME: Virus 1 is stronger than Virus 2 Prakash and Faloutsos 2012

69
**Answer: Yes! There is a phase transition**

ASSUME: Virus 1 is stronger than Virus 2 Prakash and Faloutsos 2012

70
**Answer: Yes! There is a phase transition**

ASSUME: Virus 1 is stronger than Virus 2 Prakash and Faloutsos 2012

71
**Our Result: Viruses can Co-exist**

Given our model and a fully connected graph, there exists an εcritical such that for ε ≥ εcritical, there is a fixed point where both viruses survive. Details The stronger survives only if it is above threshold Virus 1 is stronger than Virus 2, if: strength(Virus 1) > strength(Virus 2) Strength(Virus) σ = N β / δ In Beutel+ KDD 2012 Prakash and Faloutsos 2012

72
**Real Examples [Google Search Trends data] Hulu v Blockbuster**

Prakash and Faloutsos 2012

73
**Real Examples [Google Search Trends data] Chrome v Firefox**

Prakash and Faloutsos 2012

74
**Outline Motivation Part 1: Understanding Epidemics (Theory)**

Part 2: Policy and Action (Algorithms) Part 3: Learning Models (Empirical Studies) Conclusion single virus VS multiple viruses Prakash and Faloutsos 2012

75
**Part 2: Algorithms Q3: Whom to immunize? Q4: How to detect outbreaks?**

Q5: Who are the culprits? Prakash and Faloutsos 2012

76
**Full Static Immunization**

Given: a graph A, virus prop. model and budget k; Find: k ‘best’ nodes for immunization (removal). ? ? k = 2 ? Knight or armor or shield ? Prakash and Faloutsos 2012

77
**Part 2: Algorithms Q3: Whom to immunize? Q4: How to detect outbreaks?**

Full Immunization (Static Graphs) Full Immunization (Dynamic Graphs) Fractional Immunization Q4: How to detect outbreaks? Q5: Who are the culprits? Prakash and Faloutsos 2012

78
**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’? Prakash and Faloutsos 2012

79
**Proposed vulnerability measure λ**

λ is the epidemic threshold “Safe” “Vulnerable” “Deadly” Increasing λ Increasing vulnerability Prakash and Faloutsos 2012

80
**A1: “Eigen-Drop”: an ideal shield value**

Eigen-Drop(S) Δ λ = λ - λs 9 9 Δ 9 11 10 10 1 1 6 2 4 4 8 8 2 3 7 3 7 5 5 6 Original Graph Without {2, 6} Prakash and Faloutsos 2012

81
**(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! Prakash and Faloutsos 2012

82
**A2: Our Solution Part 1: Shield Value Part 2: Algorithm**

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 N == nodes M = edges In Tong+ ICDM 2010 Prakash and Faloutsos 2012

83
**Our Solution: Part 1 Details Approximate Eigen-drop (Δ λ) A u u = λ .**

Δ λ ≈ SV(S) = Result using Matrix perturbation theory u(i) == ‘eigenscore’ ~~ pagerank(i) u(i) A u u = λ . Prakash and Faloutsos 2012

84
**Details P1: node importance P2: set diversity Select by P1**

Select by P1+P2 Original Graph Prakash and Faloutsos 2012

85
**Our Solution: Part 2: NetShield**

We prove that: SV(S) is sub-modular (& monotone non-decreasing) NetShield: Greedily add best node at each step Corollary: Greedy algorithm works 1. NetShield is near-optimal (w.r.t. max SV(S)) 2. NetShield is O(nk2+m) tell jon’s paper (influence propagation..) Footnote: near-optimal means SV(S NetShield) >= (1-1/e) SV(S Opt) Prakash and Faloutsos 2012

86
**Experiment: Immunization quality**

Log(fraction of infected nodes) PageRank Betweeness (shortest path) Degree Lower is better Acquaintance Eigs (=HITS) NetShield Time Prakash and Faloutsos 2012

87
**Part 2: Algorithms Q3: Whom to immunize? Q4: How to detect outbreaks?**

Full Immunization (Static Graphs) Full Immunization (Dynamic Graphs) Fractional Immunization Q4: How to detect outbreaks? Q5: Who are the culprits? Prakash and Faloutsos 2012

88
**Full Dynamic Immunization**

Given: Set of T arbitrary graphs Find: k ‘best’ nodes to immunize (remove) day N night N , weekend….. In Prakash+ ECML-PKDD 2010 Prakash and Faloutsos 2012

89
**Full Dynamic Immunization**

day night Our solution Recall theorem Simple: reduce (= ) Goal: max eigendrop Δ No competing policy for comparison We propose and evaluate many policies Matrix Product Δ = Prakash and Faloutsos 2012

90
**Performance of Policies**

Lower is better MIT Reality Mining Prakash and Faloutsos 2012

91
**Part 2: Algorithms Q3: Whom to immunize? Q4: How to detect outbreaks?**

Full Immunization (Static Graphs) Full Immunization (Dynamic Graphs) Fractional Immunization Q4: How to detect outbreaks? Q5: Who are the culprits? Prakash and Faloutsos 2012

92
**Fractional Immunization of Networks**

B. Aditya Prakash, Lada Adamic, Theodore Iwashyna (M.D.), Hanghang Tong, Christos Faloutsos Under Submission Prakash and Faloutsos 2012

93
**Previously: Full Static Immunization**

Given: a graph A, virus prop. model and budget k; Find: k ‘best’ nodes for immunization (removal). k = 2 ? Knight or armor or shield ? Prakash and Faloutsos 2012

94
**Fractional Asymmetric Immunization**

Fractional Effect [ f(x) = ] Asymmetric Effect # antidotes = 3 Prakash and Faloutsos 2012

95
**Now: Fractional Asymmetric Immunization**

Fractional Effect [ f(x) = ] Asymmetric Effect # antidotes = 3 Prakash and Faloutsos 2012

96
**Fractional Asymmetric Immunization**

Fractional Effect [ f(x) = ] Asymmetric Effect Boxes as pills, f(1) = 0.5, f(2) =0.25… # antidotes = 3 Prakash and Faloutsos 2012

97
**Fractional Asymmetric Immunization**

Drug-resistant Bacteria (like XDR-TB) Title and authors (we were happy to have medical doctors as collaborators) Hospital Another Hospital Prakash and Faloutsos 2012

98
**Fractional Asymmetric Immunization**

Drug-resistant Bacteria (like XDR-TB) Title and authors (we were happy to have medical doctors as collaborators) Hospital Another Hospital Prakash and Faloutsos 2012

99
**Fractional Asymmetric Immunization**

Problem: Given k units of disinfectant, how to distribute them to maximize hospitals saved? Title and authors (we were happy to have medical doctors as collaborators) Hospital Another Hospital Prakash and Faloutsos 2012

100
**Our Algorithm “SMART-ALLOC”**

~6x fewer! [US-MEDICARE NETWORK 2005] Each circle is a hospital, ~3000 hospitals More than 30,000 patients transferred Faster CURRENT PRACTICE SMART-ALLOC Prakash and Faloutsos 2012

101
**≈ Running Time Wall-Clock Time > 30,000x speed-up! Lower is better**

> 1 week ≈ > 30,000x speed-up! Lower is better 14 secs Simulations SMART-ALLOC Prakash and Faloutsos 2012

102
**Experiments Lower is better SECOND-LIFE PENN-NETWORK K = 200 K = 2000**

Prakash and Faloutsos 2012

103
**Part 2: Algorithms Q3: Whom to immunize? Q4: How to detect outbreaks?**

Q5: Who are the culprits? Prakash and Faloutsos 2012

104
**Break! Terraces of hot springs and carbonate minerals**

Pamukkale, Turkey Prakash and Faloutsos 2012

105
**Part 2: Algorithms Q3: Whom to immunize? Q4: How to detect outbreaks?**

Q5: Who are the culprits? Prakash and Faloutsos 2012

106
**Outbreak detection Spot contamination points**

Minimize time to detection, population affected Maximize probability of detection. Minimize sensor placement cost. Compare each goal to blogs Blogs Posts Links Information cascade Prakash and Faloutsos 2012

107
**Outbreak detection Spot `hot blogs’**

Minimize time to detection, population affected Maximize probability of detection. Minimize sensor placement cost. Compare each goal to blogs Blogs Posts Links Information cascade Prakash and Faloutsos 2012

108
**J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, N**

J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, N. Glance. "Cost-effective Outbreak Detection in Networks” KDD 2007 Prakash and Faloutsos 2012

109
**CELF: Main idea Given: a graph G(V,E) Place the sensors**

a budget of B sensors data on how contaminations spread over the network: Place the sensors To minimize time to detect outbreak CELF algorithm uses submodularity and lazy evaluation They call the algorihtm CELF (contaminations data-- adjacency) Minimzie time to detect outbreak Similar to sensor placement-- compare water to blogs Prakash and Faloutsos 2012

110
**Blogs: Comparison to heuristics**

Benefit (higher=better) Prakash and Faloutsos 2012

111
“Best 10 blogs to read” NP - number of posts, IL- in-links, OLO- blog out links, OLA- all out links k PA score Blog NP IL OLO OLA Finds ones with small “cost” Prakash and Faloutsos 2012

112
**Part 2: Algorithms Q3: Whom to immunize? Q4: How to detect outbreaks?**

Q5: Who are the culprits? Prakash and Faloutsos 2012

113
B. Aditya Prakash, Jilles Vreeken, Christos Faloutsos ‘Detecting Culprits in Epidemics: Who and How many?’ Under Submission Prakash and Faloutsos 2012

114
**Problem definition 2-d grid ‘+’ -> infected Who started it?**

Prakash and Faloutsos 2012

115
**Problem definition 2-d grid ‘+’ -> infected Who started it?**

Prior work: [Lappas et al. 2010, Shah et al. 2011] Prakash and Faloutsos 2012

116
**Culprits: Exoneration**

Prakash and Faloutsos 2012

117
**Culprits: Exoneration**

Prakash and Faloutsos 2012

118
**Who are the culprits Two-part solution Running time =**

use MDL for number of seeds for a given number: exoneration = centrality + penalty our method uses smallest eigenvector of Laplacian submatrix Running time = linear! (in edges and nodes) Prakash and Faloutsos 2012

119
**Outline Motivation Part 1: Understanding Epidemics (Theory)**

Part 2: Policy and Action (Algorithms) Part 3: Learning Models (Empirical Studies) Conclusion single virus VS multiple viruses Prakash and Faloutsos 2012

120
**Part 3: Empirical Studies**

Q6: How do cascades look like? Q7: How does activity evolve over time? Q8: How does external influence act? Prakash and Faloutsos 2012

121
**Cascading Behavior in Large Blog Graphs**

Blogs Posts Information cascade Links How does information propagate over the blogosphere? A topic I’m fond of… J. Leskovec, M.McGlohon, C. Faloutsos, N. Glance, M. Hurst. Cascading Behavior in Large Blog Graphs. SDM 2007. Prakash and Faloutsos 2012

122
**Cascades on the Blogosphere**

1 a B2 B1 1 B2 b c 1 2 d 1 B3 3 B3 B4 B4 e Blogosphere blogs + posts Blog network links among blogs Post network links among posts Some graphs we will use a b c d Cascade is graph induced by a time ordered propagation of information (edges) e e Cascades Prakash and Faloutsos 2012

123
**Blog data 45,000 blogs participating in cascades**

All their posts for 3 months (Aug-Sept ‘05) 2.4 million posts ~5 million links (245,404 inside the dataset) Number of posts Number of posts Prakash and Faloutsos 2012 Time [1 day]

124
**Popularity over time Post popularity drops-off – exponentially?**

Faloutsos Popularity over time # in links lag: days after post 1 2 3 Post popularity drops-off – exponentially? @t @t + lag Prakash and Faloutsos 2012 124

125
**Popularity over time Post popularity drops-off – exponentially?**

Faloutsos Popularity over time # in links (log) days after post (log) Post popularity drops-off – exponentially? POWER LAW! Exponent? Prakash and Faloutsos 2012 125

126
**Popularity over time Post popularity drops-off – exponentially?**

Faloutsos Popularity over time # in links (log) -1.6 days after post (log) Post popularity drops-off – exponentially? POWER LAW! Exponent? -1.6 close to -1.5: Barabasi’s stack model and like the zero-crossings of a random walk Prakash and Faloutsos 2012 126

127
-1.5 slope J. G. Oliveira & A.-L. Barabási Human Dynamics: The Correspondence Patterns of Darwin and Einstein. Nature 437, 1251 (2005) . [PDF] Prakash and Faloutsos 2012

128
**Topological Observations**

How do we measure how information flows through the network? Common cascade shapes extracted using algorithms in [Leskovec, Singh, Kleinberg; PAKDD 2006]. Prakash and Faloutsos 2012

129
**Topological Observations**

What graph properties do cascades exhibit? Cascade size distributions also follow power law. Observation 2: The probability of observing a cascade on n nodes follows a Zipf distribution: p(n) n-2 the first two points --- singletons (don’t care), pairs (2 node chains) a=-2 Count Prakash and Faloutsos 2012 Cascade size (# of nodes)

130
**Topological Observations**

What graph properties do cascades exhibit? Stars and chains also follow a power law, with different exponents (star -3.1, chain -8.5). a=-3.1 Count a=-8.5 Chains drop off much more steeply This could be due to the time delay Count Size of star (# nodes) Size of chain (# nodes) Prakash and Faloutsos 2012

131
**to identify communities?**

Blogs and structure Cascades take on different shapes (sorted by frequency): How can we use cascades to identify communities? Prakash and Faloutsos 2012

132
**PCA on cascade types Perform PCA on sparse matrix. Use log(count+1)**

Project onto 2 PC… ~9,000 cascade types % -> ………… .01 … .07 .67 1.1 2.1 5.1 4.2 3.4 3.2 boingboing .09 4.6 slashdot ~44,000 blogs Prakash and Faloutsos 2012

133
PCA on cascade types Observation: Content of blogs and cascade behavior are often related. Distinct clusters for “conservative” and “humorous” blogs (hand-labeling). M. McGlohon, J. Leskovec, C. Faloutsos, M. Hurst, N. Glance. Finding Patterns in Blog Shapes and Blog Evolution. ICWSM 2007. 28 Prakash and Faloutsos 2012

134
PCA on cascade types Observation: Content of blogs and cascade behavior are often related. Distinct clusters for “conservative” and “humorous” blogs (hand-labeling). We think the PCs correspond o stars and chains-- humorous blogs tend to go all at once, no deep discussions. M. McGlohon, J. Leskovec, C. Faloutsos, M. Hurst, N. Glance. Finding Patterns in Blog Shapes and Blog Evolution. ICWSM 2007. 29 Prakash and Faloutsos 2012

135
**Part 3: Empirical Studies**

Q6: How do cascades look like? Q7: How does activity evolve over time? Q8: How does external influence act? Prakash and Faloutsos 2012

136
**Rise and fall patterns in social media**

Meme (# of mentions in blogs) short phrases Sourced from U.S. politics in 2008 “you can put lipstick on a pig” “yes we can” Prakash and Faloutsos 2012

137
**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] Prakash and Faloutsos 2012

138
**Rise and fall patterns in social media**

Answer: YES! We can represent all patterns by single model In Matsubara+ SIGKDD 2012 Prakash and Faloutsos 2012

139
**Main idea - SpikeM β 1. Un-informed bloggers (uninformed about rumor)**

2. External shock at time nb (e.g, breaking news) 3. Infection (word-of-mouth) β Time n=0 Time n=nb Time n=nb+1 Infectiveness of a blog-post at age n: Strength of infection (quality of news) Decay function (how infective a blog posting is) Power Law Prakash and Faloutsos 2012

140
**SpikeM - with periodicity**

Details SpikeM - with periodicity Full equation of SpikeM Periodicity 12pm Peak activity 3am Low activity Time n Bloggers change their activity over time (e.g., daily, weekly, yearly) activity Prakash and Faloutsos 2012

141
**SpikeM -> exponential**

Details Analysis – exponential rise and power-raw fall Rise-part SI -> exponential SpikeM -> exponential Liner-log Log-log Prakash and Faloutsos 2012

142
**Details SI -> exponential SpikeM -> power law**

Analysis – exponential rise and power-raw fall Fall-part SI > exponential SpikeM -> power law Liner-log Log-log Prakash and Faloutsos 2012

143
**Tail-part forecasts SpikeM can capture tail part**

Prakash and Faloutsos 2012

144
**“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 ? ? Prakash and Faloutsos 2012

145
**“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 Prakash and Faloutsos 2012

146
**Part 3: Empirical Studies**

Q6: How do cascades look like? Q7: How does activity evolve over time? Q8: How does external influence act? Prakash and Faloutsos 2012

147
**Tweets Diffusion: Problem Definition**

Given: Action log of people tweeting a #hashtag ( ) A network of users ( ) Find: How external influence varies with #hashtags? ?? ?? ? ? Prakash and Faloutsos 2012 ? ?

148
**Results: External Influence vs Time**

“External Effects” Long-running tags #nowwatching, #nowplaying, #epictweets “Word-of-mouth” #purpleglasses, #brits, #famouslies Bursty, external events #oscar, #25jan #openfollow, #ihatequotes, #tweetmyjobs “Word-of-mouth” Not trending time Can also use for Forecasting, Anomaly Detection! Prakash and Faloutsos 2012

149
**Outline Motivation Part 1: Understanding Epidemics (Theory)**

Part 2: Policy and Action (Algorithms) Part 3: Learning Models (Empirical Studies) Conclusion single virus VS multiple viruses Prakash and Faloutsos 2012

150
**Conclusions Epidemic Threshold Fast Immunization Bursts: SpikeM model**

It’s the Eigenvalue Fast Immunization Max. drop in eigenvalue, linear-time near-optimal algorithm Bursts: SpikeM model Exponential growth, Power-law decay Prakash and Faloutsos 2012

151
**Propagation on Networks**

Biology Theory & Algo. Physics Comp. Systems Propagation on Networks Social Science ML & Stats. Econ. Prakash and Faloutsos 2012

152
**References Prakash and Faloutsos 2012**

Winner-takes-all: Competing Viruses or Ideas on fair-play networks (B. Aditya Prakash, Alex Beutel, Roni Rosenfeld, Christos Faloutsos) – In WWW 2012, Lyon 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.) Times Series Clustering: Complex is Simpler! (Lei Li, B. Aditya Prakash) - In ICML 2011, Bellevue 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 Formalizing the BGP stability problem: patterns and a chaotic model (B. Aditya Prakash, Michalis Faloutsos and Christos Faloutsos) – In IEEE INFOCOM NetSciCom Workshop, 2011. On the Vulnerability of Large Graphs (Hanghang Tong, B. Aditya Prakash, Tina Eliassi-Rad and Christos Faloutsos) – In IEEE ICDM 2010, Sydney, Australia 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 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. Parsimonious Linear Fingerprinting for Time Series (Lei Li, B. Aditya Prakash and Christos Faloutsos) - In VLDB 2010, Singapore EigenSpokes: Surprising Patterns and Scalable Community Chipping in Large Graphs (B. Aditya Prakash, Ashwin Sridharan, Mukund Seshadri, Sridhar Machiraju and Christos Faloutsos) – In PAKDD 2010, Hyderabad, India BGP-lens: Patterns and Anomalies in Internet-Routing Updates (B. Aditya Prakash, Nicholas Valler, David Andersen, Michalis Faloutsos and Christos Faloutsos) – In ACM SIGKDD 2009, Paris, France. Surprising Patterns and Scalable Community Detection in Large Graphs (B. Aditya Prakash, Ashwin Sridharan, Mukund Seshadri, Sridhar Machiraju and Christos Faloutsos) – In IEEE ICDM Large Data Workshop 2009, Miami FRAPP: A Framework for high-Accuracy Privacy-Preserving Mining (Shipra Agarwal, Jayant R. Haritsa and B. Aditya Prakash) – In Intl. Journal on Data Mining and Knowledge Discovery (DKMD), Springer, vol. 18, no. 1, February 2009, Ed: Johannes Gehrke. Complex Group-By Queries For XML (C. Gokhale, N. Gupta, P. Kumar, L. V. S. Lakshmanan, R. Ng and B. Aditya Prakash) – In IEEE ICDE 2007, Istanbul, Turkey. Prakash and Faloutsos 2012

153
**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, Deepayan Chakrabarti, Hanghang Tong, Kunal Punera, Ashwin Sridharan, Sridhar Machiraju, Mukund Seshadri, Alice Zheng, Lei Li, Polo Chau, Nicholas Valler, Alex Beutel, Xuetao Wei Prakash and Faloutsos 2012

154
Acknowledgements Funding Prakash and Faloutsos 2012

155
**Dynamical Processes on Large Networks**

B. Aditya Prakash Christos Faloutsos Analysis Policy/Action Data Prakash and Faloutsos 2012

Similar presentations

OK

2 20 107 1 106 13 19 21 25 22/24 3 12 14 23 4 11 15 26 27 30 5 7 6 9 16 18 28 104 29 8/17 32/109 100 99 105 108 31 34 40/42 41 98 68 93 90/92 91 81 80.

2 20 107 1 106 13 19 21 25 22/24 3 12 14 23 4 11 15 26 27 30 5 7 6 9 16 18 28 104 29 8/17 32/109 100 99 105 108 31 34 40/42 41 98 68 93 90/92 91 81 80.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google