Social Network Under Stress · Social Network Temporal Dynamics Temporal dynamics of networks: Short diameter, densiﬁcaon, clustering, heavy tail degree distribu3on, … [Leskovec

4322 North Quad, 105 S. State St. Ann Arbor, MI 48109-1285

Social Network Under Stress

DanielM.RomeroSchoolofInforma3onUniversityofMichigan

Incollabora3onwithBrianUzziandJonKleinberg

1

SocialNetworkTemporalDynamics

2

t=1 t=2 t=3 t=4


Temporaldynamicsofnetworks:Shortdiameter,densifica3on,clustering,heavytaildegreedistribu3on,…[Leskovecetal.2007,Barabasietal.1999,Kossinetsetal.2009,…]

3

t=1 t=2 t=3 t=4


Temporaldynamicsofnetworks:Shortdiameter,densifica3on,clustering,heavytaildegreedistribu3on,…[Leskovecetal.2007,Barabasietal.1999,Kossinetsetal.2009,…]

Usefulfor:•  Linkpredic3on•  Detec3nginfluen3alnodes•  Findingcommuni3es 4

t=1 t=2 t=3 t=4


5

t=1


6

t=1 t=2


7

t=1 t=2


8

t=1 t=2

?

HedgeFundDataInstantMessages(IM):• FullrecordofIMs:content,sender,recipient,3mestamp• 182internaldecisionmakers,8646outsidecontacts• 22MillionIMs

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HedgeFundDataInstantMessages(IM):• FullrecordofIMs:content,sender,recipient,3mestamp• 182internaldecisionmakers,8646outsidecontacts• 22MillionIMs

StockTrading:• Fullrecordofalltransac3ons:stock,price,numberofstocks,typeoftransac3on(Buy,Sell),3mestamp• 600Ktrades• 2008–2012

10

MarketMovements(Shocks) SocialNetwork

11

InThisTalk


12

Trading

InThisTalk


Performance

13

Trading

InThisTalk


Emo3onalandCogni3veContent

Performance

14

Trading

InThisTalk


Emo3onalandCogni3veContent

Performance

15

Trading

InThisTalk

16

Measures

Shock:Changeinpriceofstocksondayd%change:(closing–opening)/opening

17

Measures

Foreachstocksanddayd,generatenetworkG(s,d)amongemployeeswhomen3ons


18

Measures





19

Measures



20

Measures

Network’sfeatures:• Size(Nodes,edges)



21

Measures

Network’sfeatures:• Size(Nodes,edges)• Density(Clustering



22

Measures




23

Measures




24

Measures

Network’sfeatures:• Size(Nodes,edges)• Density(Clustering,3estrength)



25

Measures




26

Measures




27

Measures

Network’sfeatures:• Size(Nodes,edges)• Density(Clustering,3estrength)• Openness(Borderedges)



28

Measures

Network’sfeatures:• Size(Nodes,edges)• Density(Clustering,3estrength)• Openness(Borderedges)



29

Measures

30

Turtled-upnetwork

31

Turtled-upnetwork

Opennetwork

Networksmayturtle-upduringshocks:•  Trust(Granovefer1985,Coleman1988)•  Exper3seknowledge,repeated

informa3onchannels(Coleman1990)•  Threatrigidity(Staw1981)

32

Theore3calExpecta3ons

Turtled-upnetwork

Opennetwork

Networksmayturtle-upduringshocks:•  Trust[Granovefer1985,Coleman1988]•  Exper3seknowledge,repeated

informa3onchannels[Coleman1990]•  Threatrigidity[Staw1981]

33

Theore3calExpecta3ons

Turtled-upnetwork

Opennetwork

Networksmayopen-upduringshocks:•  Newinforma3onthroughweak3es

[Granovefer1973]•  Diverseinforma3onfromdifferentgroups

(structuralholes)[Burt92]

Numofnodes|Past:Ra3oofnum.nodesinG(s,d)andmeannum.nodesinG(s,d’)ford’<d.

�10 �5 0 5 10

Change in stock price (%)1.4

1.6

1.8

2.0

2.2

2.4

2.6

Num

.nod

es

Num. nodes | past days

Num

.Nod

es

Changeinstockprice(%)Changeinstockprice(%)

-10010

34

Findings:Size

Numofnodes|Past:Ra3oofnum.nodesinG(s,d)andmeannum.nodesinG(s,d’)ford’<d.

�10 �5 0 5 10


1.6

1.8

2.0

2.2

2.4

2.6

Num

.nod

es

Num. nodes | past days

Num

.Nod

es


-10010

ShocksMorenodesandedges

35

Findings:Size

Findings:ClusteringCoefficient

�10 �5 0 5 10


1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Clu

ster

ing

coef

f.

Clustering coeff. | Num. EdgesClustering coeff. | Num. Nodes

AverageClusterin

gCo

efficien

t


-10010

36

Clusteringcoefficientofanoden:thera3ooftheexis3ngandpossiblenumberofedgesamongtheneighborsofn.

�10 �5 0 5 10


1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Clu

ster

ing

coef

f.


AverageClusterin

gCo

efficien

t

Changeinstockprice(%)

C=4/10

Changeinstockprice(%)-10010

37



�10 �5 0 5 10


1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Clu

ster

ing

coef

f.


AverageClusterin

gCo

efficien

t



HigherClusteringcoefficientShocks

C=4/10


38


�10 �5 0 5 10


0.482

0.484

0.486

0.488

0.490

0.492

0.494

0.496

Stre

ngth

oftie

s

Strength of ties

Percen

tageofStron

gTies

Tiestrength:(x,y)isk-strong,ifyisamongthetopk%mostfrequentconnec3onsofx


39

Findings:TieStrength

�10 �5 0 5 10


0.482

0.484

0.486

0.488

0.490

0.492

0.494

0.496

Stre

ngth

oftie

s

Strength of ties

Percen

tageofStron

gTies

HigherDestrengthShocks

Tiestrength:(x,y)isk-strong,ifyisamongthetopk%mostfrequentconnec3onsofx


40

Findings:TieStrength

�15 �10 �5 0 5 10 15


0.810

0.815

0.820

0.825

0.830

0.835

0.840

Perc

enta

geof

bord

ered

ges

Percentage of border edges

Percen

tageofB

orde

rEdges

Borderedges:involveanoutsidecontact


41

Findings:Openness

�15 �10 �5 0 5 10 15


0.810

0.815

0.820

0.825

0.830

0.835

0.840

Perc

enta

geof

bord

ered

ges

Percentage of border edges

Percen

tageofB

orde

rEdges

MoreborderedgesShocks

Borderedges:involveanoutsidecontact


42

Findings:Openness

Networks“Turtle-up”DuringShocks

Consistentwiththeoriesof:• Trust• Exper3seknowledge,repeatedinforma3onchannels• Threatrididity

•  Higherclustering•  Strongeredges•  Moreinternalcommunica3on

43

Turtled-upnetwork

LIWCCategoriesLinguisDcInquiryWordCount(LIWC):textanalysistool,whichiden3fieswordsthatbelongtovariouscategories.

AffecDveProcessesPosi3ve Love,niceNega3ve Hurt,uglyAnxiety Worried,fearfulAnger Hate,killSadness Crying,sad

CogniDveProcessesInsight Think,Consider

Causa3on Because,HenceDiscrepancy Should,CouldTenta3ve Maybe,GuessCertainty Always,NeverInhibi3on Block,ConstrainInclusive With,IncludeExclusive But,Exclude

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PriceChangesvs.Emo3ons

Changeinstockprice(%)�8 �6 �4 �2 0 2 4 6 8


0.003

0.004

0.005

0.006

0.007

0.008

0.009

Perc

enta

geof

wor

dsNega3

veEmo3

ons


Posi3vepricechangesHigherposi3veemo3ons

45

PriceChangesvs.Emo3ons



0.009

0.010

0.011

0.012

0.013

0.014

Perc

enta

geof

wor

dsPo

si3veEmo3

on


�8 �6 �4 �2 0 2 4 6 8


0.003

0.004

0.005

0.006

0.007

0.008

0.009

Perc

enta

geof

wor

dsNega3

veEmo3

ons


Nega3vepricechangesHighernega3veemo3ons

Posi3vepricechangesHigherposi3veemo3ons

Emo3onsareasymmetricwithrespecttopricechange.46

PriceChangesvs.Cogni3veProcesses

�8 �6 �4 �2 0 2 4 6 8


0.072

0.074

0.076

0.078

0.080

Perc

enta

geof

wor

dsCo

gni3veProcesses



0.0070

0.0075

0.0080

0.0085

0.0090

Perc

enta

geof

wor

dsInsig

ht


PricechangesHighercogni3velanguage

Cogni3veprocessesareasymmetricwithrespecttopricechange.



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48

Predic3ngSen3mentandCogni3on

Task:Forafixedstocksanddayd,predictifIMsthatmen3onsondaydcontainmorewordsinthecategorythanaverage.

Affective Cognitive Insight Neg. emo. Pos. emo.0.50

0.55

0.60

0.65

Acc

urac

yCombinedNetworkPrice changes

Accuracy

0.65

0.50

0.60

0.55

49




0.55

0.60

0.65

Acc

urac


Accuracy

0.65

0.50

0.60

0.55

50




0.55

0.60

0.65

Acc

urac


Accuracy

0.65

0.50

0.60

0.55

51




0.55

0.60

0.65

Acc

urac


Accuracy

0.65

0.50

0.60

0.55

52




0.55

0.60

0.65

Acc

urac


Accuracy

0.65

0.50

0.60

0.55

53




0.55

0.60

0.65

Acc

urac


Networkvariablesaremorepredic3veoftypeofcontentthanpricechanges.

Accuracy

0.65

0.50

0.60

0.55

54


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Predic3ngStockTrading

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Task:Predictwhetherastockthathasnotbeentradedforkweekswillbetraded.

Networkvariablesaremorepredic3veoftypeofsuddenstocktradingthanpricechanges.

57


Numberofweekswithoutatrade

0 1 2 3 4 5 6 7 8 9

Number of weeks without trade (k)0.50

0.55

0.60

0.65

0.70

0.75

0.80

Acc

urac

y

All features combinedPrevious Trades and NetworkPrevious Trades and PricesPrevious Trades

02468

0.80

0.70

0.60

0.50

Accuracy

Task:Predictwhetherastockthathasnotbeentradedforkweekswillbetraded.

Conclusions•  Rela3onshipbetweenstockmarketshocksandsocialnetwork

structure

•  Compe3nghypotheses:turtleupvs.opennetworkstructure

•  Communica3on“turtles-up”duringshocks.

•  Networkstructureispredic3veoftrading,performance,andemo3onalandcogni3vecontent.

•  Stockmarketchangesdonotimprovepredic3onaccuracy.

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Networkvariablesaremorepredic3veofperformancethanpricechanges.

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Predic3ngPerformance

Num.daysconsecu3vetransac3on0246

0.58

0.54

0.50

Accuracy

0 1 2 3 4 5 6

Minimum number of days of consecutive transactions0.50

0.52

0.54

0.56

0.58

0.60

Acc

urac

y

CombinedNetworkPrice changes

SubopDmaltrade:Worsepricethantheworstpricethenextday.Task:Forafixedstockstradedondayd,predictifit’ssubop3malN-serialtrades:AtradeofstocksthathasoccurredforatleastNconsecu3vedays

Documents

Social Network Under Stress · Social Network Temporal Dynamics Temporal dynamics of networks: Short diameter, densiﬁcaon, clustering, heavy tail degree distribu3on, … [Leskovec