Upload File

mining large graphs - aristotle university of thessaloniki · 2015-03-20 · mining large social...

  • Home
  • Documents
  • Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The
55
CMU SCS Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU

Upload: others

Post on 05-Jul-2020

1 views

Category:

Documents


0 download

Report

TRANSCRIPT

Page 1: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Mining Large Social Networks:

Patterns and Anomalies

Christos Faloutsos

CMU

Page 2: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Thank you

• The Department of Informatics

• Happy 20-th!

• Prof. Yannis Manolopoulos

• Prof. Kostas Tsichlas

• Mrs. Nina Daltsidou

AUTH, May 30, 2012 C. Faloutsos (CMU) 2

Page 3: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

International-caliber friends

among AUTH alumni

• Prof. Evimaria Terzi (U. Boston)

• Prof. Kyriakos Mouratidis (SMU)

• Dr. Michalis Vlachos (IBM)

• …

AUTH, May 30, 2012 C. Faloutsos (CMU) 3

Page 4: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 4

Outline

• Introduction – Motivation

• Problem#1: Patterns in graphs

• Problem#2: Tools

• Problem#3: Scalability

• Conclusions

AUTH, May 30, 2012

Page 5: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 5

Graphs - why should we care?

Internet Map

[lumeta.com]

Food Web

[Martinez ’91]

AUTH, May 30, 2012

$10s of BILLIONS revenue

>500M users

Page 6: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 6

Graphs - why should we care?

• IR: bi-partite graphs (doc-terms)

• web: hyper-text graph

• ... and more:

D1

DN

T1

TM

... ...

AUTH, May 30, 2012

Page 7: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 7

Graphs - why should we care?

• web-log (‘blog’) news propagation

• computer network security: email/IP traffic

and anomaly detection

• ....

• [subject-verb-object: graph]

• Graph == relational table with 2 columns

(src, dst)

• BIG DATA – big graphs

AUTH, May 30, 2012

Page 8: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

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

AUTH, May 30, 2012

Page 9: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

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?

AUTH, May 30, 2012

Page 10: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 10

Graph mining

• Are real graphs random?

AUTH, May 30, 2012

Page 11: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 11

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

AUTH, May 30, 2012

Page 12: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 12

Solution# S.1

• Power law in the degree distribution

[SIGCOMM99]

log(rank)

log(degree)

internet domains

att.com

ibm.com

AUTH, May 30, 2012

Page 13: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 13

Solution# S.1

• Power law in the degree distribution

[SIGCOMM99]

log(rank)

log(degree)

-0.82

internet domains

att.com

ibm.com

AUTH, May 30, 2012

Page 14: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 14

But:

How about graphs from other domains?

AUTH, May 30, 2012

Page 15: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 15

More power laws:

• web hit counts [w/ A. Montgomery]

Web Site Traffic

in-degree (log scale)

Count

(log scale) Zipf

users sites

``ebay’’

AUTH, May 30, 2012

Page 16: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

And numerous more

• Who-trusts-whom (epinions.com)

• Income [Pareto] –’80-20 distribution’

• Duration of downloads [Bestavros+]

• Duration of UNIX jobs (‘mice and

elephants’)

• Size of files of a user

• …

• ‘Black swans’

AUTH, May 30, 2012 C. Faloutsos (CMU) 16

Page 17: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 17

Outline

• Introduction – Motivation

• Problem#1: Patterns in graphs

– Static graphs

• degree, diameter, eigen,

• Triangles

– Time evolving graphs

• Problem#2: Tools

AUTH, May 30, 2012

Page 18: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 18

Solution# S.3: Triangle ‘Laws’

• Real social networks have a lot of triangles

AUTH, May 30, 2012

Page 19: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 19

Solution# S.3: Triangle ‘Laws’

• Real social networks have a lot of triangles

– Friends of friends are friends

• Any patterns?

AUTH, May 30, 2012

Page 20: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 20

Triangle Law: #S.3 [Tsourakakis ICDM 2008]

SN Reuters

Epinions X-axis: degree

Y-axis: mean # triangles

n friends -> ~n1.6 triangles

AUTH, May 30, 2012

Page 21: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Triangle counting for large graphs?

Anomalous nodes in Twitter(~ 3 billion edges)

[U Kang, Brendan Meeder, +, PAKDD’11]

21 AUTH, May 30, 2012 21 C. Faloutsos (CMU)

Page 22: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Triangle counting for large graphs?

Anomalous nodes in Twitter(~ 3 billion edges)

[U Kang, Brendan Meeder, +, PAKDD’11]

22 AUTH, May 30, 2012 22 C. Faloutsos (CMU)

Page 23: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Triangle counting for large graphs?

Anomalous nodes in Twitter(~ 3 billion edges)

[U Kang, Brendan Meeder, +, PAKDD’11]

23 AUTH, May 30, 2012 23 C. Faloutsos (CMU)

Page 24: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 24

Outline

• Introduction – Motivation

• Problem#1: Patterns in graphs

– Static graphs

– Time evolving graphs

• Problem#2: Tools

• …

AUTH, May 30, 2012

Page 25: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 25

Problem: Time evolution

• with Jure Leskovec (CMU ->

Stanford)

• and Jon Kleinberg (Cornell –

sabb. @ CMU)

AUTH, May 30, 2012

Page 26: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 26

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?

AUTH, May 30, 2012

Page 27: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 27

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

AUTH, May 30, 2012

Page 28: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 28

T.1 Diameter – “Patents”

• Patent citation

network

• 25 years of data

• @1999

– 2.9 M nodes

– 16.5 M edges

time [years]

diameter

AUTH, May 30, 2012

Page 29: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 29

Outline

• Introduction – Motivation

• Problem#1: Patterns in graphs

• Problem#2: Tools

– Belief Propagation

• Problem#3: Scalability

• Conclusions

AUTH, May 30, 2012

Page 30: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

AUTH, May 30, 2012 C. Faloutsos (CMU) 30

E-bay Fraud detection

w/ Polo Chau &

Shashank Pandit, CMU

[www’07]

Page 31: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

AUTH, May 30, 2012 C. Faloutsos (CMU) 31

E-bay Fraud detection

Page 32: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

AUTH, May 30, 2012 C. Faloutsos (CMU) 32

E-bay Fraud detection

Page 33: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

AUTH, May 30, 2012 C. Faloutsos (CMU) 33

E-bay Fraud detection - NetProbe

Page 34: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Popular press

And less desirable attention:

• E-mail from ‘Belgium police’ (‘copy of

your code?’)

AUTH, May 30, 2012 C. Faloutsos (CMU) 34

Page 35: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 35

Outline

• Introduction – Motivation

• Problem#1: Patterns in graphs

• Problem#2: Tools

• Problem#3: Scalability -PEGASUS

• Conclusions

AUTH, May 30, 2012

Page 36: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

AUTH, May 30, 2012 C. Faloutsos (CMU) 36

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/
Page 37: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 37

Outline

• Introduction – Motivation

• Problem#1: Patterns in graphs

• Problem#2: Tools

• Problem#3: Scalability –PEGASUS

– Radius plot

• Conclusions

AUTH, May 30, 2012

Page 38: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

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) 38 AUTH, May 30, 2012

Page 39: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

????

19+ [Barabasi+]

39 C. Faloutsos (CMU)

Radius

Count

AUTH, May 30, 2012

~1999, ~1M nodes

Page 40: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges)

• Largest publicly available graph ever studied.

????

19+ [Barabasi+]

40 C. Faloutsos (CMU)

Radius

Count

AUTH, May 30, 2012

??

~1999, ~1M nodes

�

�

Page 41: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges)

• Largest publicly available graph ever studied.

????

19+? [Barabasi+]

41 C. Faloutsos (CMU)

Radius

Count

AUTH, May 30, 2012

14 (dir.)

~7 (undir.)

Page 42: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges)

•7 degrees of separation (!)

•Diameter: shrunk

????

19+? [Barabasi+]

42 C. Faloutsos (CMU)

Radius

Count

AUTH, May 30, 2012

14 (dir.)

~7 (undir.)

Page 43: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges)

Q: Shape?

????

43 C. Faloutsos (CMU)

Radius

Count

AUTH, May 30, 2012

~7 (undir.)

Page 44: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

44 C. Faloutsos (CMU)

YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges)

• effective diameter: surprisingly small.

• Multi-modality (?!) AUTH, May 30, 2012

Page 45: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Radius Plot of GCC of YahooWeb.

45 C. Faloutsos (CMU) AUTH, May 30, 2012

Page 46: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

46 C. Faloutsos (CMU)

YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges)

• effective diameter: surprisingly small.

• Multi-modality: probably mixture of cores . AUTH, May 30, 2012

Page 47: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

47 C. Faloutsos (CMU)

YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges)

• effective diameter: surprisingly small.

• Multi-modality: probably mixture of cores . AUTH, May 30, 2012

EN

~7

Conjecture:

DE

BR

Page 48: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

48 C. Faloutsos (CMU)

YahooWeb graph (120Gb, 1.4B nodes, 6.6 B edges)

• effective diameter: surprisingly small.

• Multi-modality: probably mixture of cores . AUTH, May 30, 2012

~7

Conjecture:

Page 49: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 49

Outline

• Introduction – Motivation

• Problem#1: Patterns in graphs

• Problem#2: Tools

• Problem#3: Scalability

• Conclusions

AUTH, May 30, 2012

Page 50: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 50

OVERALL CONCLUSIONS –

low level:

• Several new patterns (shrinking diameters,

triangle-laws, etc)

• New tools:

– Fraud detection (belief propagation)

• Scalability: PEGASUS / hadoop

AUTH, May 30, 2012

Page 51: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 51

OVERALL CONCLUSIONS –

medium-level

• BIG DATA: Large datasets reveal patterns/outliers that are invisible otherwise

AUTH, May 30, 2012

Page 52: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

C. Faloutsos (CMU) 52

Project info

Akoglu,

Leman

Chau,

Polo

Kang, U McGlohon,

Mary

Tong,

Hanghang

Prakash,

Aditya

AUTH, May 30, 2012

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

Page 53: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Thank you for the honor!

• Congratulations for 20-th anniversary

and…

AUTH, May 30, 2012 C. Faloutsos (CMU) 53

Page 54: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

High-level conclusion:

Collaborations

• Sociology + CS (triangles)

• Civil engineering + CS (sensor placement)

• fMRI/medical + graphs (medical db’s)

• …

AUTH, May 30, 2012 C. Faloutsos (CMU) 54

Page 55: Mining large graphs - Aristotle University of Thessaloniki · 2015-03-20 · Mining Large Social Networks: Patterns and Anomalies Christos Faloutsos CMU . CMU SCS Thank you • The

CMU SCS

Never stop learning

AUTH, May 30, 2012 C. Faloutsos (CMU) 55

Socrates Plato Aristotle

GHRASKW AEI DIDASKOMENOS


Mining Large Graphschristos/TALKS/16-09-AMAZON/faloutsos... · 2016. 9. 9. · CMU SCS Mining Large Graphs: Patterns, Anomalies, and Fraud Detection Christos Faloutsos CMU

Large Graph Miningchristos/TALKS/11-Vertica/FOILS/... · 2011. 8. 7. · CMU SCS Large Graph Mining Christos Faloutsos CMU . CMU SCS Thank you! • Stephen Walkauskas Vertica'11 C

CMU SCS Data Mining on Streams Christos Faloutsos CMU

Large Graph Mining - Patterns, Explanations and Cascade ... › ~electriconf › 2013 › ... · CMU SCS Large Graph Mining - Patterns, Explanations and Cascade Analysis Christos

CMU SCS Large Graph Mining - Patterns, Tools and Cascade Analysis Christos Faloutsos CMU

Large Graph Mining - Department of Computer Science and ... › ~hillol › NGDM07 › abstracts › ... · Large Graph Mining Christos Faloutsos CMU. NGDM 2007 C. Faloutsos 2 CMU

CMU SCS Sensor data mining and forecasting Christos Faloutsos CMU [email protected]

Social Networks and Graph Mining Christos Faloutsos CMU - MLD

Part 1: Graph Mining – patternschristos/TALKS/17-CMU-Tepper-Alan/faloutsos-P1-patterns.pdf(C) C. Faloutsos, 2017 4 CMU SCS Graph mining • Are real graphs random? Tepper, CMU, April

CMU SCS Data Mining Meets Systems: Tools and Case Studies Christos Faloutsos SCS CMU

Mining Large Graphs and Fraud Detectionchristos/TALKS/15-03-Qatar/MLDAS/faloutsos_MLD… · Mining Large Graphs and Fraud Detection Christos Faloutsos CMU . ... Deployment at Facebook

Large Graph Mining - Patterns, Explanations and Cascade ... · CMU SCS Large Graph Mining - Patterns, Explanations and Cascade Analysis Christos Faloutsos CMU

CMU SCS Mining Billion-node Graphs Christos Faloutsos CMU

Large Graph Mining – Patterns, Tools and Cascade …Large Graph Mining – Patterns, Tools and Cascade analysis Christos Faloutsos CMU CMU SCS Thank you! • Brian Gallagher •

CMU SCS Large Graph Mining Christos Faloutsos CMU

Mining Large Graphs and Fraud Detectionchristos/TALKS/15-03-Qatar/FOILS-dept-seminar/... · Mining Large Graphs and Fraud Detection Christos Faloutsos CMU . ... Deployment at Facebook

CMU SCS KDD'09Faloutsos, Miller, Tsourakakis P0-1 Large Graph Mining: Power Tools and a Practitioner’s guide Christos Faloutsos Gary Miller Charalampos

CMU SCS Graph and stream mining Christos Faloutsos CMU

CMU SCS Thank you! C. Faloutsos CMU. CMU SCS Large Graph Mining C. Faloutsos CMU

Tools for large graph miningjure/talks/...CMU SCS Tools for large graph mining WWW 2008 tutorial Part 3: Matrix tools for graph mining Jure Leskovec and Christos Faloutsos Machine

CMU SCS Graph Mining and Influence Propagation Christos Faloutsos CMU

Large Graph Mining - Carnegie Mellon School of Computer ... › ~christos › TALKS › 11-SFU › faloutsos... · Large Graph Mining Christos Faloutsos CMU . CMU SCS Thank you! •

Large Graph Mining - CMUctsourak/KDD09/FOILS/task7_theory.pdf · CMU SCS Large Graph Mining: Power Tools and a Practitioner’s Guide Christos Faloutsos Gary Miller Charalampos (Babis)

CMU SCS Finding patterns in large, real networks Christos Faloutsos CMU

CMU SCS Large Graph Mining - Patterns, Explanations and Cascade Analysis Christos Faloutsos CMU

CMU SCS Mining Graphs and Tensors Christos Faloutsos CMU

CMU SCS KDD'09Faloutsos, Miller, Tsourakakis P3-1 Large Graph Mining: Power Tools and a Practitioner’s guide Task 3: Recommendations & proximity Faloutsos,

Data mining - detailed outlinechristos/courses/dbms.S12/slides/99data… · Data Warehousing / Data Mining (R&G, ch 25 and 26) CMU SCS Faloutsos CMU SCS 15-415 2 Data mining - detailed

Large Graph Mining: Power Tools and a Practitioner’s guidectsourak/KDD09/FOILS/task0_intro.pdf · CMU SCS Large Graph Mining: Power Tools and a Practitioner’s guide Christos Faloutsos

CMU SCS C. Faloutsos (CMU)#1 Large Graph Algorithms Christos Faloutsos CMU McGlohon, Mary Prakash, Aditya Tong, Hanghang Tsourakakis, Babis Akoglu, Leman

Mining Billion-Node Graphs - Patterns and Algorithms Billion... · 2014. 4. 18. · CMU SCS Mining Billion-Node Graphs - Patterns and Algorithms Christos Faloutsos CMU . ... CMU SCS

CMU SCS Graph Mining: Laws, Generators and Tools Christos Faloutsos CMU

CMU SCS Multimedia and Graph mining Christos Faloutsos CMU

Large Graph Mining - Patterns, Explanations and Cascade ... › ~christos › TALKS › 13-08-asonam › FOILS... · CMU SCS Large Graph Mining - Patterns, Explanations and Cascade

Large Graph Mining - Patterns, Explanations and Cascade ...christos/TALKS/15-03-Qatar/FOILS-gm-tutorial/fal… · CMU SCS Large Graph Mining - Patterns, Explanations and Cascade Analysis