1. CMU SCS Mining Billion-Node Graphs: Patterns and Algorithms Christos Faloutsos CMU

2. CMU SCS Thank you! Dr. Ching-Hao (Eric) Mao Prof. Kenneth Pao Taiwan, Aug'12C. Faloutsos (CMU) 2

3. CMU SCS C. Faloutsos (CMU) 3 Our goal: Open source system for mining huge graphs: PEGASUS project (PEta GrAph mining System) www.cs.cmu.edu/~pegasus code and papers Taiwan, Aug'12

4. CMU SCS C. Faloutsos (CMU) 4 Outline Introduction – Motivation Problem#1: Patterns in graphs Problem#2: Tools Problem#3: Scalability Conclusions Taiwan, Aug'12

5. CMU SCS C. Faloutsos (CMU) 5 Graphs - why should we care? Internet Map [lumeta.com] Food Web [Martinez ’91] Friendship Network [Moody ’01] Taiwan, Aug'12

6. CMU SCS C. Faloutsos (CMU) 6 Graphs - why should we care? IR: bi-partite graphs (doc-terms) web: hyper-text graph... and more: D1D1 DNDN T1T1 TMTM... Taiwan, Aug'12

7. CMU SCS C. Faloutsos (CMU) 7 Graphs - why should we care? ‘viral’ marketing web-log (‘blog’) news propagation computer network security: email/IP traffic and anomaly detection.... Taiwan, Aug'12

8. CMU SCS C. Faloutsos (CMU) 8 Outline Introduction – Motivation Problem#1: Patterns in graphs –Static graphs –Weighted graphs –Time evolving graphs Problem#2: Tools Problem#3: Scalability Conclusions Taiwan, Aug'12

9. CMU SCS C. Faloutsos (CMU) 9 Problem #1 - network and graph mining What does the Internet look like? What does FaceBook look like? What is ‘normal’/‘abnormal’? which patterns/laws hold? Taiwan, Aug'12

10. CMU SCS C. Faloutsos (CMU) 10 Problem #1 - network and graph mining What does the Internet look like? What does FaceBook look like? What is ‘normal’/‘abnormal’? which patterns/laws hold? –To spot anomalies (rarities), we have to discover patterns Taiwan, Aug'12

11. CMU SCS C. Faloutsos (CMU) 11 Problem #1 - network and graph mining What does the Internet look like? What does FaceBook look like? What is ‘normal’/‘abnormal’? which patterns/laws hold? –To spot anomalies (rarities), we have to discover patterns –Large datasets reveal patterns/anomalies that may be invisible otherwise… Taiwan, Aug'12

12. CMU SCS C. Faloutsos (CMU) 12 Graph mining Are real graphs random? Taiwan, Aug'12

13. CMU SCS C. Faloutsos (CMU) 13 Laws and patterns Are real graphs random? A: NO!! –Diameter –in- and out- degree distributions –other (surprising) patterns So, let’s look at the data Taiwan, Aug'12

14. CMU SCS C. Faloutsos (CMU) 14 Solution# S.1 Power law in the degree distribution [SIGCOMM99] log(rank) log(degree) internet domains att.com ibm.com Taiwan, Aug'12

15. CMU SCS C. Faloutsos (CMU) 15 Solution# S.1 Power law in the degree distribution [SIGCOMM99] log(rank) log(degree) -0.82 internet domains att.com ibm.com Taiwan, Aug'12

16. CMU SCS C. Faloutsos (CMU) 16 Solution# S.2: Eigen Exponent E A2: power law in the eigenvalues of the adjacency matrix E = -0.48 Exponent = slope Eigenvalue Rank of decreasing eigenvalue May 2001 Taiwan, Aug'12

17. CMU SCS C. Faloutsos (CMU) 17 Solution# S.2: Eigen Exponent E [Mihail, Papadimitriou ’02]: slope is ½ of rank exponent E = -0.48 Exponent = slope Eigenvalue Rank of decreasing eigenvalue May 2001 Taiwan, Aug'12

18. CMU SCS C. Faloutsos (CMU) 18 But: How about graphs from other domains? Taiwan, Aug'12

19. CMU SCS C. Faloutsos (CMU) 19 More power laws: web hit counts [w/ A. Montgomery] Web Site Traffic in-degree (log scale) Count (log scale) Zipf users sites ``ebay’’ Taiwan, Aug'12

20. CMU SCS C. Faloutsos (CMU) 20 epinions.com who-trusts-whom [Richardson + Domingos, KDD 2001] (out) degree count trusts-2000-people user Taiwan, Aug'12

21. CMU SCS And numerous more # of sexual contacts Income [Pareto] –’80-20 distribution’ Duration of downloads [Bestavros+] Duration of UNIX jobs (‘mice and elephants’) Size of files of a user … ‘Black swans’ Taiwan, Aug'12C. Faloutsos (CMU) 21

22. CMU SCS C. Faloutsos (CMU) 22 Outline Introduction – Motivation Problem#1: Patterns in graphs –Static graphs degree, diameter, eigen, triangles cliques –Weighted graphs –Time evolving graphs Problem#2: Tools Taiwan, Aug'12

23. CMU SCS C. Faloutsos (CMU) 23 Solution# S.3: Triangle ‘Laws’ Real social networks have a lot of triangles Taiwan, Aug'12

24. CMU SCS C. Faloutsos (CMU) 24 Solution# S.3: Triangle ‘Laws’ Real social networks have a lot of triangles –Friends of friends are friends Any patterns? Taiwan, Aug'12

25. CMU SCS C. Faloutsos (CMU) 25 Triangle Law: #S.3 [Tsourakakis ICDM 2008] ASNHEP-TH Epinions X-axis: # of participating triangles Y: count (~ pdf) Taiwan, Aug'12

26. CMU SCS C. Faloutsos (CMU) 26 Triangle Law: #S.3 [Tsourakakis ICDM 2008] ASNHEP-TH Epinions Taiwan, Aug'12 X-axis: # of participating triangles Y: count (~ pdf)

27. CMU SCS C. Faloutsos (CMU) 27 Triangle Law: #S.4 [Tsourakakis ICDM 2008] SNReuters Epinions X-axis: degree Y-axis: mean # triangles n friends -> ~n 1.6 triangles Taiwan, Aug'12

28. CMU SCS C. Faloutsos (CMU) 28 Triangle Law: Computations [Tsourakakis ICDM 2008] But: triangles are expensive to compute (3-way join; several approx. algos) Q: Can we do that quickly? details Taiwan, Aug'12

29. CMU SCS C. Faloutsos (CMU) 29 Triangle Law: Computations [Tsourakakis ICDM 2008] But: triangles are expensive to compute (3-way join; several approx. algos) Q: Can we do that quickly? A: Yes! #triangles = 1/6 Sum ( i 3 ) (and, because of skewness (S2), we only need the top few eigenvalues! details Taiwan, Aug'12

30. CMU SCS C. Faloutsos (CMU) 30 Triangle Law: Computations [Tsourakakis ICDM 2008] 1000x+ speed-up, >90% accuracy details Taiwan, Aug'12

31. CMU SCS Triangle counting for large graphs? Anomalous nodes in Twitter(~ 3 billion edges) [U Kang, Brendan Meeder, +, PAKDD’11] 31 Taiwan, Aug'12 31 C. Faloutsos (CMU)

32. CMU SCS Triangle counting for large graphs? Anomalous nodes in Twitter(~ 3 billion edges) [U Kang, Brendan Meeder, +, PAKDD’11] 32 Taiwan, Aug'12 32 C. Faloutsos (CMU)

33. CMU SCS EigenSpokes B. Aditya Prakash, Mukund Seshadri, Ashwin Sridharan, Sridhar Machiraju and Christos Faloutsos: EigenSpokes: Surprising Patterns and Scalable Community Chipping in Large Graphs, PAKDD 2010, Hyderabad, India, 21-24 June 2010. C. Faloutsos (CMU) 33 Taiwan, Aug'12

34. CMU SCS EigenSpokes Eigenvectors of adjacency matrix  equivalent to singular vectors (symmetric, undirected graph) 34 C. Faloutsos (CMU)Taiwan, Aug'12

35. CMU SCS EigenSpokes Eigenvectors of adjacency matrix  equivalent to singular vectors (symmetric, undirected graph) 35 C. Faloutsos (CMU)Taiwan, Aug'12 N N details

36. CMU SCS EigenSpokes Eigenvectors of adjacency matrix  equivalent to singular vectors (symmetric, undirected graph) 36 C. Faloutsos (CMU)Taiwan, Aug'12 N N details

37. CMU SCS EigenSpokes Eigenvectors of adjacency matrix  equivalent to singular vectors (symmetric, undirected graph) 37 C. Faloutsos (CMU)Taiwan, Aug'12 N N details

38. CMU SCS EigenSpokes Eigenvectors of adjacency matrix  equivalent to singular vectors (symmetric, undirected graph) 38 C. Faloutsos (CMU)Taiwan, Aug'12 N N details

39. CMU SCS EigenSpokes EE plot: Scatter plot of scores of u1 vs u2 One would expect –Many points @ origin –A few scattered ~randomly C. Faloutsos (CMU) 39 u1 u2 Taiwan, Aug'12 1 st Principal component 2 nd Principal component

40. CMU SCS EigenSpokes EE plot: Scatter plot of scores of u1 vs u2 One would expect –Many points @ origin –A few scattered ~randomly C. Faloutsos (CMU) 40 u1 u2 90 o Taiwan, Aug'12

41. CMU SCS EigenSpokes - pervasiveness Present in mobile social graph  across time and space Patent citation graph 41 C. Faloutsos (CMU)Taiwan, Aug'12

42. CMU SCS EigenSpokes - explanation Near-cliques, or near- bipartite-cores, loosely connected 42 C. Faloutsos (CMU)Taiwan, Aug'12

43. CMU SCS EigenSpokes - explanation Near-cliques, or near- bipartite-cores, loosely connected 43 C. Faloutsos (CMU)Taiwan, Aug'12

44. CMU SCS EigenSpokes - explanation Near-cliques, or near- bipartite-cores, loosely connected 44 C. Faloutsos (CMU)Taiwan, Aug'12

45. CMU SCS EigenSpokes - explanation Near-cliques, or near- bipartite-cores, loosely connected So what?  Extract nodes with high scores  high connectivity  Good “communities” spy plot of top 20 nodes 45 C. Faloutsos (CMU)Taiwan, Aug'12

46. CMU SCS Bipartite Communities! magnified bipartite community patents from same inventor(s) `cut-and-paste’ bibliography! 46 C. Faloutsos (CMU)Taiwan, Aug'12

47. CMU SCS (maybe, botnets?) Victim IPs? Botnet members? 47 C. Faloutsos (CMU)Taiwan, Aug'12 Exploring this direction with Dr. Mao and III.

48. CMU SCS C. Faloutsos (CMU) 48 Outline Introduction – Motivation Problem#1: Patterns in graphs –Static graphs degree, diameter, eigen, triangles cliques –Weighted graphs –Time evolving graphs Problem#2: Tools Taiwan, Aug'12

49. CMU SCS C. Faloutsos (CMU) 49 Observations on weighted graphs? A: yes - even more ‘laws’! M. McGlohon, L. Akoglu, and C. Faloutsos Weighted Graphs and Disconnected Components: Patterns and a Generator. SIG-KDD 2008 Taiwan, Aug'12

50. CMU SCS C. Faloutsos (CMU) 50 Observation W.1: Fortification Q: How do the weights of nodes relate to degree? Taiwan, Aug'12

51. CMU SCS C. Faloutsos (CMU) 51 Observation W.1: Fortification More donors, more $ ? $10 $5 Taiwan, Aug'12 ‘Reagan’ ‘Clinton’ $7

52. CMU SCS Edges (# donors) In-weights ($) C. Faloutsos (CMU) 52 Observation W.1: fortification: Snapshot Power Law Weight: super-linear on in-degree exponent ‘iw’: 1.01 < iw < 1.26 Orgs-Candidates e.g. John Kerry, $10M received, from 1K donors More donors, even more $ $10 $5 Taiwan, Aug'12

53. CMU SCS C. Faloutsos (CMU) 53 Outline Introduction – Motivation Problem#1: Patterns in graphs –Static graphs –Weighted graphs –Time evolving graphs Problem#2: Tools … Taiwan, Aug'12

54. CMU SCS C. Faloutsos (CMU) 54 Problem: Time evolution with Jure Leskovec (CMU -> Stanford) and Jon Kleinberg (Cornell – sabb. @ CMU) Taiwan, Aug'12

55. CMU SCS C. Faloutsos (CMU) 55 T.1 Evolution of the Diameter Prior work on Power Law graphs hints at slowly growing diameter: –diameter ~ O(log N) –diameter ~ O(log log N) What is happening in real data? Taiwan, Aug'12

56. CMU SCS C. Faloutsos (CMU) 56 T.1 Evolution of the Diameter Prior work on Power Law graphs hints at slowly growing diameter: –diameter ~ O(log N) –diameter ~ O(log log N) What is happening in real data? Diameter shrinks over time Taiwan, Aug'12

57. CMU SCS C. Faloutsos (CMU) 57 T.1 Diameter – “Patents” Patent citation network 25 years of data @1999 –2.9 M nodes –16.5 M edges time [years] diameter Taiwan, Aug'12

58. CMU SCS C. Faloutsos (CMU) 58 T.2 Temporal Evolution of the Graphs N(t) … nodes at time t E(t) … edges at time t Suppose that N(t+1) = 2 * N(t) Q: what is your guess for E(t+1) =? 2 * E(t) Taiwan, Aug'12

59. CMU SCS C. Faloutsos (CMU) 59 T.2 Temporal Evolution of the Graphs N(t) … nodes at time t E(t) … edges at time t Suppose that N(t+1) = 2 * N(t) Q: what is your guess for E(t+1) =? 2 * E(t) A: over-doubled! –But obeying the ``Densification Power Law’’ Taiwan, Aug'12

60. CMU SCS C. Faloutsos (CMU) 60 T.2 Densification – Patent Citations Citations among patents granted @1999 –2.9 M nodes –16.5 M edges Each year is a datapoint N(t) E(t) 1.66 Taiwan, Aug'12

61. CMU SCS C. Faloutsos (CMU) 61 Outline Introduction – Motivation Problem#1: Patterns in graphs –Static graphs –Weighted graphs –Time evolving graphs Problem#2: Tools … Taiwan, Aug'12

62. CMU SCS C. Faloutsos (CMU) 62 More on Time-evolving graphs M. McGlohon, L. Akoglu, and C. Faloutsos Weighted Graphs and Disconnected Components: Patterns and a Generator. SIG-KDD 2008 Taiwan, Aug'12

63. CMU SCS C. Faloutsos (CMU) 63 Observation T.3: NLCC behavior Q: How do NLCC’s emerge and join with the GCC? (``NLCC’’ = non-largest conn. components) –Do they continue to grow in size? – or do they shrink? – or stabilize? Taiwan, Aug'12

64. CMU SCS C. Faloutsos (CMU) 64 Observation T.3: NLCC behavior Q: How do NLCC’s emerge and join with the GCC? (``NLCC’’ = non-largest conn. components) –Do they continue to grow in size? – or do they shrink? – or stabilize? Taiwan, Aug'12

65. CMU SCS C. Faloutsos (CMU) 65 Observation T.3: NLCC behavior Q: How do NLCC’s emerge and join with the GCC? (``NLCC’’ = non-largest conn. components) –Do they continue to grow in size? – or do they shrink? – or stabilize? Taiwan, Aug'12 YES

66. CMU SCS C. Faloutsos (CMU) 66 Observation T.3: NLCC behavior After the gelling point, the GCC takes off, but NLCC’s remain ~constant (actually, oscillate). IMDB CC size Time-stamp Taiwan, Aug'12

67. CMU SCS C. Faloutsos (CMU) 67 Timing for Blogs with Mary McGlohon (CMU->Google) Jure Leskovec (CMU->Stanford) Natalie Glance (now at Google) Mat Hurst (now at MSR) [SDM’07] Taiwan, Aug'12

68. CMU SCS C. Faloutsos (CMU) 68 T.4 : popularity over time Post popularity drops-off – exponentially? lag: days after post # in links 1 2 3 @t @t + lag Taiwan, Aug'12

69. CMU SCS C. Faloutsos (CMU) 69 T.4 : popularity over time Post popularity drops-off – exponentially? POWER LAW! Exponent? # in links (log) days after post (log) Taiwan, Aug'12

70. CMU SCS C. Faloutsos (CMU) 70 T.4 : popularity over time 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 # in links (log) -1.6 days after post (log) Taiwan, Aug'12

71. CMU SCS Taiwan, Aug'12C. Faloutsos (CMU) 71 -1.5 slope J. G. Oliveira & A.-L. Barabási Human Dynamics: The Correspondence Patterns of Darwin and Einstein. Nature 437, 1251 (2005). [PDF]PDF

72. CMU SCS T.5: duration of phonecalls Surprising Patterns for the Call Duration Distribution of Mobile Phone Users Pedro O. S. Vaz de Melo, Leman Akoglu, Christos Faloutsos, Antonio A. F. Loureiro PKDD 2010 Taiwan, Aug'12C. Faloutsos (CMU) 72

73. CMU SCS Probably, power law (?) Taiwan, Aug'12C. Faloutsos (CMU) 73 ??

74. CMU SCS No Power Law! Taiwan, Aug'12C. Faloutsos (CMU) 74

75. CMU SCS ‘TLaC: Lazy Contractor’ The longer a task (phonecall) has taken, The even longer it will take Taiwan, Aug'12C. Faloutsos (CMU) 75 Odds ratio= Casualties(<x): Survivors(>=x) == power law

76. CMU SCS 76 Data Description Data from a private mobile operator of a large city 4 months of data 3.1 million users more than 1 billion phone records Over 96% of ‘talkative’ users obeyed a TLAC distribution (‘talkative’: >30 calls) Taiwan, Aug'12C. Faloutsos (CMU)

77. CMU SCS Outliers: Taiwan, Aug'12C. Faloutsos (CMU) 77

78. CMU SCS C. Faloutsos (CMU) 78 Outline Introduction – Motivation Problem#1: Patterns in graphs Problem#2: Tools –OddBall (anomaly detection) –Belief Propagation –Immunization Problem#3: Scalability Conclusions Taiwan, Aug'12

79. CMU SCS OddBall: Spotting A n o m a l i e s in Weighted Graphs Leman Akoglu, Mary McGlohon, Christos Faloutsos Carnegie Mellon University School of Computer Science PAKDD 2010, Hyderabad, India

80. CMU SCS 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) 80 Taiwan, Aug'12

81. CMU SCS What is an egonet? ego 81 egonet C. Faloutsos (CMU)Taiwan, Aug'12

82. CMU SCS Selected Features  N i : number of neighbors (degree) of ego i  E i : number of edges in egonet i  W i : total weight of egonet i  λ w,i : principal eigenvalue of the weighted adjacency matrix of egonet I 82 C. Faloutsos (CMU)Taiwan, Aug'12

83. CMU SCS Near-Clique/Star 83 Taiwan, Aug'12C. Faloutsos (CMU)

84. CMU SCS Near-Clique/Star 84 C. Faloutsos (CMU)Taiwan, Aug'12

85. CMU SCS Near-Clique/Star 85 C. Faloutsos (CMU)Taiwan, Aug'12

86. CMU SCS Andrew Lewis (director) Near-Clique/Star 86 C. Faloutsos (CMU)Taiwan, Aug'12

87. CMU SCS C. Faloutsos (CMU) 87 Outline Introduction – Motivation Problem#1: Patterns in graphs Problem#2: Tools –OddBall (anomaly detection) –Belief Propagation –Immunization Problem#3: Scalability Conclusions Taiwan, Aug'12

88. CMU SCS Taiwan, Aug'12C. Faloutsos (CMU) 88 E-bay Fraud detection w/ Polo Chau & Shashank Pandit, CMU [www’07]

89. CMU SCS Taiwan, Aug'12C. Faloutsos (CMU) 89 E-bay Fraud detection

90. CMU SCS Taiwan, Aug'12C. Faloutsos (CMU) 90 E-bay Fraud detection

91. CMU SCS Taiwan, Aug'12C. Faloutsos (CMU) 91 E-bay Fraud detection - NetProbe

92. CMU SCS Popular press And less desirable attention: E-mail from ‘Belgium police’ (‘copy of your code?’) Taiwan, Aug'12C. Faloutsos (CMU) 92

93. CMU SCS C. Faloutsos (CMU) 93 Outline Introduction – Motivation Problem#1: Patterns in graphs Problem#2: Tools –OddBall (anomaly detection) –Belief Propagation – antivirus app –Immunization Problem#3: Scalability Conclusions Taiwan, Aug'12

94. CMU SCS Polo Chau Machine Learning Dept Carey Nachenberg Vice President & Fellow Jeffrey Wilhelm Principal Software Engineer Adam Wright Software Engineer Prof. Christos Faloutsos Computer Science Dept Polonium: Tera-Scale Graph Mining and Inference for Malware Detection PATENT PENDING SDM 2011, Mesa, Arizona

95. CMU SCS Polonium: The Data 60+ terabytes of data anonymously contributed by participants of worldwide Norton Community Watch program 50+ million machines 900+ million executable files Constructed a machine-file bipartite graph (0.2 TB+) 1 billion nodes (machines and files) 37 billion edges Taiwan, Aug'12 95 C. Faloutsos (CMU)

96. CMU SCS 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 Taiwan, Aug'12 96 C. Faloutsos (CMU)

97. CMU SCS Polonium: One-Interaction Results 84.9% True Positive Rate 1% False Positive Rate True Positive Rate % of malware correctly identified False Positive Rate % of non-malware wrongly labeled as malware 97 Ideal Taiwan, Aug'12C. Faloutsos (CMU)

98. CMU SCS C. Faloutsos (CMU) 98 Outline Introduction – Motivation Problem#1: Patterns in graphs Problem#2: Tools –[OddBall (anomaly detection)] –[Belief propagation] –[Immunization] –Visualization Problem#3: Scalability -PEGASUS Conclusions Taiwan, Aug'12

99. CMU SCS Apolo Making Sense of Large Network Data: Combining Rich User Interaction & Machine Learning CHI 2011, Vancouver, Canada Polo ChauProf. Niki Kittur Prof. Jason Hong Prof. Christos Faloutsos Taiwan, Aug'12 99 C. Faloutsos (CMU)

100. CMU SCS Main Ideas of Apolo Provides a mixed-initiative approach (ML + HCI) to help users interactively explore large graphs Users start with small subgraph, then iteratively expand: 1.User specifies exemplars 2.Belief Propagation to find other relevant nodes User study showed Apolo outperformed Google Scholar in making sense of citation network data Taiwan, Aug'12 100 C. Faloutsos (CMU)

101. CMU SCS Exemplars Rest of the nodes are considered relevant (by BP); relevance indicated by color saturation. Note that BP supports multiple groups Taiwan, Aug'12 101 C. Faloutsos (CMU)

102. CMU SCS C. Faloutsos (CMU) 102 Outline Introduction – Motivation Problem#1: Patterns in graphs Problem#2: Tools –OddBall (anomaly detection) –Belief propagation –Immunization Problem#3: Scalability -PEGASUS Conclusions Taiwan, Aug'12

103. CMU SCS Taiwan, Aug'12C. Faloutsos (CMU) 103 Scalability Google: > 450,000 processors in clusters of ~2000 processors each [ Barroso, Dean, Hölzle, “Web Search for a Planet: The Google Cluster Architecture” IEEE Micro 2003 ] Yahoo: 5Pb of data [Fayyad, KDD’07] Problem: machine failures, on a daily basis How to parallelize data mining tasks, then? A: map/reduce – hadoop (open-source clone) http://hadoop.apache.org/ http://hadoop.apache.org/

104. CMU SCS C. Faloutsos (CMU) 104 CentralizedHadoop/PEG ASUS Degree Distr. old Pagerank old Diameter/ANF old HERE Conn. Comp old HERE TrianglesdoneHERE Visualizationstarted Outline – Algorithms & results Taiwan, Aug'12

105. CMU SCS HADI for diameter estimation Radius Plots for Mining Tera-byte Scale Graphs U Kang, Charalampos Tsourakakis, Ana Paula Appel, Christos Faloutsos, Jure Leskovec, SDM’10 Naively: diameter needs O(N**2) space and up to O(N**3) time – prohibitive (N~1B) Our HADI: linear on E (~10B) –Near-linear scalability wrt # machines –Several optimizations -> 5x faster C. Faloutsos (CMU) 105 Taiwan, Aug'12

106. CMU SCS ???? 19+ [Barabasi+] 106 C. Faloutsos (CMU) Radius Count Taiwan, Aug'12 ~1999, ~1M nodes

107. CMU SCS YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges) Largest publicly available graph ever studied. ???? 19+ [Barabasi+] 107 C. Faloutsos (CMU) Radius Count Taiwan, Aug'12 ?? ~1999, ~1M nodes

108. CMU SCS YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges) Largest publicly available graph ever studied. ???? 19+? [Barabasi+] 108 C. Faloutsos (CMU) Radius Count Taiwan, Aug'12 14 (dir.) ~7 (undir.)

109. CMU SCS YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges) 7 degrees of separation (!) Diameter: shrunk ???? 19+? [Barabasi+] 109 C. Faloutsos (CMU) Radius Count Taiwan, Aug'12 14 (dir.) ~7 (undir.)

110. CMU SCS YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges) Q: Shape? ???? 110 C. Faloutsos (CMU) Radius Count Taiwan, Aug'12 ~7 (undir.)

111. CMU SCS 111 C. Faloutsos (CMU) YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges) effective diameter: surprisingly small. Multi-modality (?!) Taiwan, Aug'12

112. CMU SCS Radius Plot of GCC of YahooWeb. 112 C. Faloutsos (CMU)Taiwan, Aug'12

113. CMU SCS 113 C. Faloutsos (CMU) YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges) effective diameter: surprisingly small. Multi-modality: probably mixture of cores. Taiwan, Aug'12

114. CMU SCS 114 C. Faloutsos (CMU) YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges) effective diameter: surprisingly small. Multi-modality: probably mixture of cores. Taiwan, Aug'12 EN ~7 Conjecture: DE BR

115. CMU SCS 115 C. Faloutsos (CMU) YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges) effective diameter: surprisingly small. Multi-modality: probably mixture of cores. Taiwan, Aug'12 ~7 Conjecture:

116. CMU SCS Running time - Kronecker and Erdos-Renyi Graphs with billions edges. details

117. CMU SCS C. Faloutsos (CMU) 117 CentralizedHadoop/PEG ASUS Degree Distr. old Pagerank old Diameter/ANF old HERE Conn. Comp old HERE Triangles HERE Visualizationstarted Outline – Algorithms & results Taiwan, Aug'12

118. CMU SCS Generalized Iterated Matrix Vector Multiplication (GIMV) C. Faloutsos (CMU) 118 PEGASUS: A Peta-Scale Graph Mining System - Implementation and ObservationsSystem - Implementation and Observations. U Kang, Charalampos E. Tsourakakis, and Christos Faloutsos. (ICDM) 2009, Miami, Florida, USA. Best Application Paper (runner-up).ICDM Taiwan, Aug'12

119. CMU SCS Generalized Iterated Matrix Vector Multiplication (GIMV) C. Faloutsos (CMU) 119 PageRank proximity (RWR) Diameter Connected components (eigenvectors, Belief Prop. … ) Matrix – vector Multiplication (iterated) Taiwan, Aug'12 details

120. CMU SCS 120 Example: GIM-V At Work Connected Components – 4 observations: Size Count C. Faloutsos (CMU) Taiwan, Aug'12

121. CMU SCS 121 Example: GIM-V At Work Connected Components Size Count C. Faloutsos (CMU) Taiwan, Aug'12 1) 10K x larger than next

122. CMU SCS 122 Example: GIM-V At Work Connected Components Size Count C. Faloutsos (CMU) Taiwan, Aug'12 2) ~0.7B singleton nodes

123. CMU SCS 123 Example: GIM-V At Work Connected Components Size Count C. Faloutsos (CMU) Taiwan, Aug'12 3) SLOPE!

124. CMU SCS 124 Example: GIM-V At Work Connected Components Size Count 300-size cmpt X 500. Why? 1100-size cmpt X 65. Why? C. Faloutsos (CMU) Taiwan, Aug'12 4) Spikes!

125. CMU SCS 125 Example: GIM-V At Work Connected Components Size Count suspicious financial-advice sites (not existing now) C. Faloutsos (CMU) Taiwan, Aug'12

126. CMU SCS 126 GIM-V At Work Connected Components over Time LinkedIn: 7.5M nodes and 58M edges Stable tail slope after the gelling point C. Faloutsos (CMU)Taiwan, Aug'12

127. CMU SCS C. Faloutsos (CMU) 127 Outline Introduction – Motivation Problem#1: Patterns in graphs Problem#2: Tools Problem#3: Scalability Conclusions Taiwan, Aug'12

128. CMU SCS C. Faloutsos (CMU) 128 OVERALL CONCLUSIONS – low level: Several new patterns (fortification, triangle-laws, conn. components, etc) New tools: –anomaly detection (OddBall), belief propagation, immunization Scalability: PEGASUS / hadoop Taiwan, Aug'12

129. CMU SCS C. Faloutsos (CMU) 129 OVERALL CONCLUSIONS – high level BIG DATA: Large datasets reveal patterns/outliers that are invisible otherwise Taiwan, Aug'12

130. CMU SCS C. Faloutsos (CMU) 130 References Leman Akoglu, Christos Faloutsos: RTG: A Recursive Realistic Graph Generator Using Random Typing. ECML/PKDD (1) 2009: 13-28 Deepayan Chakrabarti, Christos Faloutsos: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1): (2006) Taiwan, Aug'12

131. CMU SCS C. Faloutsos (CMU) 131 References Deepayan Chakrabarti, Yang Wang, Chenxi Wang, Jure Leskovec, Christos Faloutsos: Epidemic thresholds in real networks. ACM Trans. Inf. Syst. Secur. 10(4): (2008) Deepayan Chakrabarti, Jure Leskovec, Christos Faloutsos, Samuel Madden, Carlos Guestrin, Michalis Faloutsos: Information Survival Threshold in Sensor and P2P Networks. INFOCOM 2007: 1316-1324 Taiwan, Aug'12

132. CMU SCS C. Faloutsos (CMU) 132 References Christos Faloutsos, Tamara G. Kolda, Jimeng Sun: Mining large graphs and streams using matrix and tensor tools. Tutorial, SIGMOD Conference 2007: 1174 Taiwan, Aug'12

133. CMU SCS C. Faloutsos (CMU) 133 References T. G. Kolda and J. Sun. Scalable Tensor Decompositions for Multi-aspect Data Mining. In: ICDM 2008, pp. 363-372, December 2008. Taiwan, Aug'12

134. CMU SCS C. Faloutsos (CMU) 134 References Jure Leskovec, Jon Kleinberg and Christos Faloutsos Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations, KDD 2005 (Best Research paper award). Jure Leskovec, Deepayan Chakrabarti, Jon M. Kleinberg, Christos Faloutsos: Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication. PKDD 2005: 133-145 Taiwan, Aug'12

135. CMU SCS C. Faloutsos (CMU) 135 References Jimeng Sun, Yinglian Xie, Hui Zhang, Christos Faloutsos. Less is More: Compact Matrix Decomposition for Large Sparse Graphs, SDM, Minneapolis, Minnesota, Apr 2007. Jimeng Sun, Spiros Papadimitriou, Philip S. Yu, and Christos Faloutsos, GraphScope: Parameter- free Mining of Large Time-evolving Graphs ACM SIGKDD Conference, San Jose, CA, August 2007 Taiwan, Aug'12

136. CMU SCS References Jimeng Sun, Dacheng Tao, Christos Faloutsos: Beyond streams and graphs: dynamic tensor analysis. KDD 2006: 374- 383 Taiwan, Aug'12C. Faloutsos (CMU) 136

137. CMU SCS C. Faloutsos (CMU) 137 References Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan, Fast Random Walk with Restart and Its Applications, ICDM 2006, Hong Kong. Hanghang Tong, Christos Faloutsos, Center-Piece Subgraphs: Problem Definition and Fast Solutions, KDD 2006, Philadelphia, PA Taiwan, Aug'12

138. CMU SCS C. Faloutsos (CMU) 138 References Hanghang Tong, Christos Faloutsos, Brian Gallagher, Tina Eliassi-Rad: Fast best-effort pattern matching in large attributed graphs. KDD 2007: 737-746 Taiwan, Aug'12

139. CMU SCS C. Faloutsos (CMU) 139 Project info Akoglu, Leman Chau, Polo Kang, U McGlohon, Mary Tong, Hanghang Prakash, Aditya Taiwan, Aug'12 Thanks to: NSF IIS-0705359, IIS-0534205, CTA-INARC ; Yahoo (M45), LLNL, IBM, SPRINT, Google, INTEL, HP, iLab www.cs.cmu.edu/~pegasus Koutra, Danae