CMU SCS Mining Large Graphs: Patterns, Anomalies, and Fraud Detection
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1 Mining Large Graphs: Patterns, Anomalies, and Fraud Detection Christos Faloutsos CMU
2 Thank you! Prof. Julian McAuley Nicholas Urioste UCSD'16 (c) 2016, C. Faloutsos 2
3 Roadmap Introduction Motivation Why study (big) graphs? Part#1: Patterns in graphs Part#2: time-evolving graphs; tensors Conclusions UCSD'16 (c) 2016, C. Faloutsos 3
4 Graphs - why should we care? ~1B nodes (web sites) ~6B edges (http links) YahooWeb graph UCSD'16 (c) 2016, C. Faloutsos 4
5 Graphs - why should we care? >$10B; ~1B users UCSD'16 (c) 2016, C. Faloutsos 5
![com] Food Web [Martinez 91]](/docs-images/81/84202065/images/6-1.jpg "UCSD'16 (c) 2016, C.")
6 Graphs - why should we care? Internet Map [lumeta.com] Food Web [Martinez 91] UCSD'16 (c) 2016, C. Faloutsos 6
7 Graphs - why should we care? web-log ( blog ) news propagation computer network security: /ip traffic and anomaly detection Recommendation systems... Many-to-many db relationship -> graph UCSD'16 (c) 2016, C. Faloutsos 7
8 Motivating problems P1: patterns? Fraud detection? P2: patterns in time-evolving graphs / tensors destination time UCSD'16 (c) 2016, C. Faloutsos 8
9 Motivating problems P1: patterns? Fraud detection? Patterns anomalies P2: patterns in time-evolving graphs / tensors destination time UCSD'16 (c) 2016, C. Faloutsos 9
10 Motivating problems P1: patterns? Fraud detection? Patterns anomalies* P2: patterns in time-evolving graphs / tensors destination time * Robust Random Cut Forest Based Anomaly Detection on Streams Sudipto Guha, Nina Mishra, Gourav Roy, UCSD'16 (c) 2016, C. Faloutsos 10 Okke Schrijvers, ICML 16
11 Roadmap Introduction Motivation Why study (big) graphs? Part#1: Patterns & fraud detection Part#2: time-evolving graphs; tensors Conclusions UCSD'16 (c) 2016, C. Faloutsos 11
12 Part 1: Patterns, & fraud detection UCSD'16 (c) 2016, C. Faloutsos 12
13 Laws and patterns Q1: Are real graphs random? UCSD'16 (c) 2016, C. Faloutsos 13
14 Laws and patterns Q1: Are real graphs random? A1: NO!! Diameter ( 6 degrees ; Kevin Bacon ) in- and out- degree distributions other (surprising) patterns So, let s look at the data UCSD'16 (c) 2016, C. Faloutsos 14
15 Solution# S.1 Power law in the degree distribution [Faloutsos x 3 SIGCOMM99] log(degree) ibm.com internet domains att.com log(rank) UCSD'16 (c) 2016, C. Faloutsos 15
16 Solution# S.1 Power law in the degree distribution [Faloutsos x 3 SIGCOMM99] log(degree) ibm.com internet domains att.com log(rank) UCSD'16 (c) 2016, C. Faloutsos 16
17 S2: connected component sizes Connected Components 4 observations: Count 1.4B nodes 6B edges UCSD'16 Size (c) 2016, C. Faloutsos 17
18 S2: connected component sizes Connected Components Count 1) 10K x larger than next UCSD'16 Size (c) 2016, C. Faloutsos 18
19 S2: connected component sizes Connected Components Count 2) ~0.7B singleton nodes UCSD'16 Size (c) 2016, C. Faloutsos 19
20 S2: connected component sizes Connected Components Count 3) SLOPE! UCSD'16 Size (c) 2016, C. Faloutsos 20
21 S2: connected component sizes Connected Components Count 300-size cmpt X size cmpt Why? X 65. Why? 4) Spikes! UCSD'16 Size (c) 2016, C. Faloutsos 21
22 S2: connected component sizes Connected Components Count suspicious financial-advice sites (not existing now) UCSD'16 Size (c) 2016, C. Faloutsos 22
23 Roadmap Introduction Motivation Part#1: Patterns in graphs Patterns: Degree; Triangles Anomaly/fraud detection Graph understanding Part#2: time-evolving graphs; tensors Conclusions UCSD'16 (c) 2016, C. Faloutsos 23
24 Solution# S.3: Triangle Laws Real social networks have a lot of triangles UCSD'16 (c) 2016, C. Faloutsos 24
25 Solution# S.3: Triangle Laws Real social networks have a lot of triangles Friends of friends are friends Any patterns? 2x the friends, 2x the triangles? UCSD'16 (c) 2016, C. Faloutsos 25
26 Triangle Law: #S.3 [Tsourakakis ICDM 2008] Reuters SN Epinions X-axis: degree Y-axis: mean # triangles n friends -> ~n 1.6 triangles UCSD'16 (c) 2016, C. Faloutsos 26
27 Triangle counting for large graphs???? Anomalous nodes in Twitter(~ 3 billion edges) [U Kang, Brendan Meeder, +, PAKDD 11] UCSD'16 (c) 2016, C. Faloutsos 27
28 Triangle counting for large graphs? Anomalous nodes in Twitter(~ 3 billion edges) [U Kang, Brendan Meeder, +, PAKDD 11] UCSD'16 (c) 2016, C. Faloutsos 28
29 Triangle counting for large graphs? Anomalous nodes in Twitter(~ 3 billion edges) [U Kang, Brendan Meeder, +, PAKDD 11] UCSD'16 (c) 2016, C. Faloutsos 29
30 Triangle counting for large graphs? Anomalous nodes in Twitter(~ 3 billion edges) [U Kang, Brendan Meeder, +, PAKDD 11] UCSD'16 (c) 2016, C. Faloutsos 30
31 Triangle counting for large graphs? Anomalous nodes in Twitter(~ 3 billion edges) [U Kang, Brendan Meeder, +, PAKDD 11] UCSD'16 (c) 2016, C. Faloutsos 31
32 MORE Graph Patterns RTG: A Recursive Realistic Graph Generator using Random UCSD'16 (c) 2016, C. Faloutsos 32 Typing Leman Akoglu and Christos Faloutsos. PKDD 09.
33 MORE Graph Patterns Mary McGlohon, Leman Akoglu, Christos Faloutsos. Statistical Properties of Social Networks. in "Social Network Data Analytics (Ed.: Charu Aggarwal) Deepayan Chakrabarti and Christos Faloutsos, Graph Mining: Laws, Tools, and Case Studies Oct. 2012, Morgan Claypool. UCSD'16 (c) 2016, C. Faloutsos 33
34 Roadmap Introduction Motivation Part#1: Patterns in graphs Patterns Anomaly / fraud detection No labels spectral Patterns With labels: Belief Propagation Part#2: time-evolving graphs; tensors Conclusions anomalies UCSD'16 (c) 2016, C. Faloutsos 34
35 How to find suspicious groups? blocks are normal, right? idols fans UCSD'16 (c) 2016, C. Faloutsos 35
36 Except that: blocks are normal, right? hyperbolic communities are more realistic [Araujo+, PKDD 14] UCSD'16 (c) 2016, C. Faloutsos 36
37 Except that: blocks are usually suspicious hyperbolic communities are more realistic [Araujo+, PKDD 14] Q: Can we spot blocks, easily? UCSD'16 (c) 2016, C. Faloutsos 37
38 Except that: blocks are usually suspicious hyperbolic communities are more realistic [Araujo+, PKDD 14] Q: Can we spot blocks, easily? A: Silver bullet: SVD! UCSD'16 (c) 2016, C. Faloutsos 38
39 Crush intro to SVD Recall: (SVD) matrix factorization: finds blocks M idols music lovers singers sports lovers athletes citizens politicians N fans ~ + + UCSD'16 (c) 2016, C. Faloutsos 39
40 Crush intro to SVD Recall: (SVD) matrix factorization: finds blocks M idols music lovers singers sports lovers athletes citizens politicians N fans ~ + + UCSD'16 (c) 2016, C. Faloutsos 40
41 Inferring Strange Behavior from Connectivity Pattern in Social Networks Meng Jiang, Peng Cui, Shiqiang Yang (Tsinghua, Beijing) Alex Beutel, Christos Faloutsos (CMU)
42 Lockstep and Spectral Subspace Plot Case #0: No lockstep behavior in random power law graph of 1M nodes, 3M edges Random Scatter Adjacency Matrix Spectral Subspace Plot UCSD'16 (c) 2016, C. Faloutsos 42
43 Lockstep and Spectral Subspace Plot Case #1: non-overlapping lockstep Blocks Rays Adjacency Matrix Spectral Subspace Plot UCSD'16 (c) 2016, C. Faloutsos 43
44 Lockstep and Spectral Subspace Plot Case #2: non-overlapping lockstep Blocks; low density Elongation Adjacency Matrix Spectral Subspace Plot UCSD'16 (c) 2016, C. Faloutsos 44
45 Lockstep and Spectral Subspace Plot Case #3: non-overlapping lockstep Camouflage (or Fame ) Rays Adjacency Matrix Tilting Spectral Subspace Plot UCSD'16 (c) 2016, C. Faloutsos 45
46 Lockstep and Spectral Subspace Plot Case #3: non-overlapping lockstep Camouflage (or Fame ) Rays Adjacency Matrix Tilting Spectral Subspace Plot UCSD'16 (c) 2016, C. Faloutsos 46
47 Lockstep and Spectral Subspace Plot Case #4:? lockstep? Pearls Adjacency Matrix Spectral Subspace Plot? UCSD'16 (c) 2016, C. Faloutsos 47
48 Tencent Weibo Dataset 117 million nodes (with profile and UGC data) 3.33 billion directed edges UCSD'16 (c) 2016, C. Faloutsos 49
49 Rays Real Data Block Pearls Staircase UCSD'16 (c) 2016, C. Faloutsos 50
50 Real Data Spikes on the out-degree distribution UCSD'16 (c) 2016, C. Faloutsos 51
51 Roadmap Introduction Motivation Part#1: Patterns in graphs Patterns Anomaly / fraud detection No labels spectral methods Suspiciousness With labels: Belief Propagation Part#2: time-evolving graphs; tensors Conclusions UCSD'16 (c) 2016, C. Faloutsos 52
52 Suspicious Patterns in Event Data 2-modes n-modes??? A General Suspiciousness Metric for Dense Blocks in Multimodal Data, Meng Jiang, Alex Beutel, Peng Cui, Bryan UCSD'16 (c) 2016, C. Faloutsos 53 Hooi, Shiqiang Yang, and Christos Faloutsos, ICDM, 2015.
53 ICDM 2015 Suspicious Patterns in Event Data 20,000 Users Retweeting same 20 tweets 6 times each All in 10 hours Which is more suspicious? vs. 225 Users Retweeting same 1 tweet 15 times each All in 3 hours All from 2 IP addresses Answer: volume * D KL (p p background ) UCSD'16 (c) 2016, C. Faloutsos 54
54 rossspot CMU SCS ICDM 2015 HOSVD Suspicious Patterns in Event Data ) 6 0 blocks VD seed in finding HOSVD. ever, the cks such though omposiata: We number 2=1, We data, so, d mass 225 Users 200 Minutes (a) Robustness 27,313 Retweets 10:09 10:14 10:19 10:24 10:32 10:37 11:04 15:37 21:13 John123 l : : l l l Joe456 l :(b) l Convergence : : : : Job789 l : l : l l l Jack135 : l : : : l : l Jill246 : l l l : Jane314 : : l l l l l Jay357 l l l l : l Fig. 4. CROSSSPOT is robust to the number of random seeds. In detecting the 4 low-order blocks, when we use 41 seeds, the best F1 score has reported the final result of as many as 1,000 seeds. CROSSSPOT converges very fast: the average number. of iterations is Fig. 1. Density multiple modes is suspicious. Left: A dense block of 225 Retweeting: users on Tencent Weibo Galaxy (Chinese Twitter) Note retweeting Dream one tweet Project: 27,313 times TABLE from 2 IPVI. addresses BIG over DENSE 200 minutes. BLOCKS Right: WITH magnification TOP METRIC of a subset VALUES of this block. Happy l and DISCOVERED : Happy indicate the INLife two THEIPRETWEETING Traveling addresses used. DATASET. Notice the how World synchronized the behavior is, across several modes (IP-address, user-id, timestamp). # User tweet IP minute Mass c Suspiciousness Informal Problem ,114 1 (Suspiciousness score) 41,396Given 1,239,865 a K- mode dataset 2 (tensor) X, with counts 27,313 of events (that 777,781 are non-negative 3 integer ,872 values), and two subtensors 17,701 Y 1 and 491,323 Y 2, which is more 1 suspicious and worthy of further 3,582 investigation? 131,113 CROSSSPOT HOSVD ,942 74, , ,211 Why multimodal data (tensor): Graphs and social networks have attracted huge interest - and they are perfectly modeled as K=2 mode datasets, that is, matrices. With K=2 modes we can model Twitter s who-follows-whom network [1][2], Face- UCSD'16 (c) 2016, C. Faloutsos 55 Figure 4(a) shows the best F1 score for different numbers of random seeds. We find that when we use 41 random seeds, the...
55 Roadmap Introduction Motivation Part#1: Patterns in graphs Patterns Anomaly / fraud detection No labels spectral methods With labels: Belief Propagation Part#2: time-evolving graphs; tensors Conclusions UCSD'16 (c) 2016, C. Faloutsos 56
56 E-bay Fraud detection w/ Polo Chau & Shashank Pandit, CMU [www 07] UCSD'16 (c) 2016, C. Faloutsos 57
57 E-bay Fraud detection UCSD'16 (c) 2016, C. Faloutsos 58
58 E-bay Fraud detection UCSD'16 (c) 2016, C. Faloutsos 59
59 E-bay Fraud detection - NetProbe UCSD'16 (c) 2016, C. Faloutsos 60
60 Popular press And less desirable attention: from Belgium police ( copy of your code? ) UCSD'16 (c) 2016, C. Faloutsos 61
61 Roadmap Introduction Motivation Part#1: Patterns in graphs Patterns Anomaly / fraud detection No labels - Spectral methods w/ labels: Belief Propagation closed formulas Part#2: time-evolving graphs; tensors Conclusions UCSD'16 (c) 2016, C. Faloutsos 62
62 Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms Danai Koutra* U Kang Hsing-Kuo Kenneth Pao Tai-You Ke Duen Horng (Polo) Chau Christos Faloutsos (*KDD dissertation award, 2016) ECML PKDD, 5-9 September 2011, Athens, Greece
63 Problem Definition: GBA techniques Given: Graph; & few labeled nodes Find: labels of rest (assuming network effects) UCSD'16 (c) 2016, C. Faloutsos 64
64 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 UCSD'16 (c) 2016, C. Faloutsos 65
65 Are they related? YES! 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 UCSD'16 (c) 2016, C. Faloutsos 66
![SSL [I + a(d - A)] x = y FABP [I + a D - c](/docs-images/81/84202065/images/66-1.jpg "A] b h = φ h 1 1 1 d1 d2 d3 0 1 0 1 0 1 0 1")
66 Correspondence of Methods Method Matrix Unknown known RWR [I c AD -1 ] x = (1-c)y SSL [I + a(d - A)] x = y FABP [I + a D - c A] b h = φ h d1 d2 d adjacency matrix? final labels/ beliefs 0 1 prior labels/ beliefs UCSD'16 (c) 2016, C. Faloutsos 67
67 Problem: e-commerce ratings fraud Given a heterogeneous graph on users, products, sellers and positive/negative ratings with seed labels Find the top k most fraudulent users, products and sellers Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos, ZooBP: Belief Propagation for Heterogeneous Networks, In UCSD'16 (c) 2016, C. Faloutsos 68 submission to VLDB 2017
68 ZooBP: Salient features Fast approximate heterogeneous belief propagation with precise convergence guarantees! Near-perfect accuracy Scalable: linear in graph size Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos, ZooBP: Belief Propagation for Heterogeneous Networks, In UCSD'16 (c) 2016, C. Faloutsos 69 submission to VLDB 2017
69 Summary of Part#1 *many* patterns in real graphs Power-laws everywhere Long (and growing) list of tools for anomaly/ fraud detection Patterns anomalies UCSD'16 (c) 2016, C. Faloutsos 70
70 Roadmap Introduction Motivation Part#1: Patterns in graphs Part#2: time-evolving graphs; tensors P2.1: time-evolving graphs P2.2: inter-arrival time patterns Conclusions UCSD'16 (c) 2016, C. Faloutsos 71
71 Part 2: Time evolving graphs; tensors UCSD'16 (c) 2016, C. Faloutsos 72
72 Graphs over time -> tensors! Problem #2.1: Given who calls whom, and when Find patterns / anomalies smith UCSD'16 (c) 2016, C. Faloutsos 73
73 Graphs over time -> tensors! Problem #2.1: Given who calls whom, and when Find patterns / anomalies UCSD'16 (c) 2016, C. Faloutsos 74
74 Graphs over time -> tensors! Problem #2.1: Given who calls whom, and when Find patterns / anomalies Tue Mon UCSD'16 (c) 2016, C. Faloutsos 75
75 Graphs over time -> tensors! Problem #2.1: Given who calls whom, and when Find patterns / anomalies caller callee UCSD'16 (c) 2016, C. Faloutsos 76
76 Graphs over time -> tensors! Problem #2.1 : Given author-keyword-date Find patterns / anomalies MANY more settings, author with >2 modes keyword UCSD'16 (c) 2016, C. Faloutsos 77
77 Graphs over time -> tensors! Problem #2.1 : Given subject verb object facts Find patterns / anomalies MANY more settings, subject with >2 modes object UCSD'16 (c) 2016, C. Faloutsos 78
78 Graphs over time -> tensors! Problem #2.1 : Given <triplets> Find patterns / anomalies MANY more settings, mode1 with >2 modes (and 4, 5, etc modes) mode2 UCSD'16 (c) 2016, C. Faloutsos 79
79 Roadmap Introduction Motivation Part#1: Patterns in graphs Part#2: time-evolving graphs; tensors P2.1: time-evolving graphs P2.2: inter-arrival time patterns Conclusions UCSD'16 (c) 2016, C. Faloutsos 82
80 Crush intro to SVD Recall: (SVD) matrix factorization: finds blocks M idols music lovers singers sports lovers athletes citizens politicians N fans ~ + + UCSD'16 (c) 2016, C. Faloutsos 83
81 Answer to both: tensor factorization PARAFAC decomposition politicians artists athletes subject = + + object UCSD'16 (c) 2016, C. Faloutsos 84
82 Answer: tensor factorization PARAFAC decomposition Results for who-calls-whom-when 4M x 15 days?????? caller = + + callee UCSD'16 (c) 2016, C. Faloutsos 85
83 Anomaly detection in timeevolving graphs = Anomalous communities in phone call data: European country, 4M clients, data over 2 weeks 1 caller 5 receivers 4 days of activity ~200 calls to EACH receiver on EACH day! UCSD'16 (c) 2016, C. Faloutsos 86
84 Anomaly detection in timeevolving graphs = Anomalous communities in phone call data: European country, 4M clients, data over 2 weeks 1 caller 5 receivers 4 days of activity ~200 calls to EACH receiver on EACH day! UCSD'16 (c) 2016, C. Faloutsos 87
85 Anomaly detection in timeevolving graphs = Anomalous communities in phone call data: European country, 4M clients, data over 2 weeks Miguel Araujo, Spiros Papadimitriou, Stephan Günnemann, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos Papalexakis, Danai Koutra. Com2: Fast ~200 calls to EACH receiver on EACH day! Automatic Discovery of Temporal (Comet) Communities. UCSD'16 (c) 2016, C. Faloutsos 88 PAKDD 2014, Tainan, Taiwan.
86 Roadmap Introduction Motivation Why study (big) graphs? Part#1: Patterns in graphs Part#2: time-evolving graphs; P2.1: tensors P2.2: Inter-arrival time patterns Acknowledgements and Conclusions UCSD'16 (c) 2016, C. Faloutsos 89
87 Universidade de São Paulo KDD 2015 Sydney, Australia RSC: Mining and Modeling Temporal Activity in Social Media Alceu F. Costa * Yuto Yamaguchi Agma J. M. Traina Caetano Traina Jr. Christos Faloutsos * alceufc@icmc.usp.br
88 Pattern Mining: Datasets Reddit Dataset Time-stamp from comments 21,198 users 20 Million time-stamps Twitter Dataset Time-stamp from tweets 6,790 users 16 Million time-stamps For each user we have: Sequence of postings time-stamps: T = (t 1, t 2, t 3, ) Inter-arrival times (IAT) of postings: ( 1, 2, 3, ) UCSD'16 t 1 t 2 t 3 t 4 (c) 2016, C. Faloutsos time 91
89 Pattern Mining Pattern 1: Distribution of IAT is heavy-tailed Users can be inactive for long periods of time before making new postings IAT Complementary Cumulative Distribution Function (CCDF) (log-log axis) CCDF, P(x ) UCSD' day 10 days CCDF, P(x ) , IAT (seconds) (c) 2016, C. Faloutsos Reddit Users day10 days , IAT (seconds) Twitter Users 92
90 Pattern Mining Pattern 1: Distribution of IAT is heavy-tailed Users can be inactive for long periods of time before making new postings No surprises IAT Complementary Should Cumulative we Distribution give up? Function (CCDF) (log-log axis) CCDF, P(x ) UCSD' day 10 days CCDF, P(x ) , IAT (seconds) (c) 2016, C. Faloutsos Reddit Users day10 days , IAT (seconds) Twitter Users 93
91 Human? Robots? linear log UCSD'16 (c) 2016, C. Faloutsos 95
92 linear Human? Robots? 2 3h 1day log UCSD'16 (c) 2016, C. Faloutsos 96
93 Experiments: Can RSC-Spotter Detect Bots? Precision vs. Sensitivity Curves Good performance: curve close to the top Precision Sensitivity (Recall) UCSD'16 RSC Spotter IAT Hist. Twitter Entropy [6] Weekday Hist. (c) 2016, C. Faloutsos Precision > 94% Sensitivity > 70% With strongly imbalanced datasets # humans >> # bots All Features 97
94 Experiments: Can RSC-Spotter Detect Bots? Precision vs. Sensitivity Curves Good performance: curve close to the top Precision Reddit Sensitivity (Recall) Precision > 96% Sensitivity > 47% With strongly imbalanced datasets # humans >> # bots UCSD'16 RSC Spotter IAT Hist. Entropy [6] Weekday Hist. (c) 2016, C. Faloutsos All Features 98
95 Part 2: Conclusions Time-evolving / heterogeneous graphs -> tensors PARAFAC finds patterns (GigaTensor/HaTen2 -> fast & scalable) = UCSD'16 (c) 2016, C. Faloutsos 99
96 Roadmap Introduction Motivation Why study (big) graphs? Part#1: Patterns in graphs Part#2: time-evolving graphs; tensors Acknowledgements and Conclusions UCSD'16 (c) 2016, C. Faloutsos 100
97 Thanks Disclaimer: All opinions are mine; not necessarily reflecting the opinions of the funding agencies Thanks to: NSF IIS , IIS , CTA-INARC; Yahoo (M45), LLNL, IBM, SPRINT, UCSD'16 (c) 2016, C. Faloutsos 101 Google, INTEL, HP, ilab
98 Cast Akoglu, Leman Araujo, Miguel Beutel, Alex Chau, Polo Eswaran, Dhivya Hooi, Bryan Kang, U Koutra, Danai Papalexakis, Vagelis Shah, Neil Shin, Kijung Song, Hyun Ah UCSD'16 (c) 2016, C. Faloutsos 102
99 CONCLUSION#1 Big data Patterns Anomalies Large datasets reveal patterns/outliers that are invisible otherwise UCSD'16 (c) 2016, C. Faloutsos 103
100 CONCLUSION#2 tensors powerful tool = 1 caller 5 receivers 4 days of activity UCSD'16 (c) 2016, C. Faloutsos 104
101 References D. Chakrabarti, C. Faloutsos: Graph Mining Laws, Tools and Case Studies, Morgan Claypool S00449ED1V01Y201209DMK006 UCSD'16 (c) 2016, C. Faloutsos 105
102 TAKE HOME MESSAGE: Cross-disciplinarity = UCSD'16 (c) 2016, C. Faloutsos 106
103 Thank you! Cross-disciplinarity = UCSD'16 (c) 2016, C. Faloutsos 107
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