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Dynamics of Internet-mediated partnership formation
8.
Computers can analyze survey data. Compared
with the internal working speed of a computer, the rate of
operation of its peripheral equipment—in particular, the
input and output mechanisms—is slow. (Simpson)
1961
9.
1966
Computer matching studies. Despite the evidence
on the romantic nature of women ... the present data
indicate that in a first dating situation, men more often
than women experience romantic attraction for their
partners. (Coombs & Kenkel, J. Marriage & Family)
Computers can analyze survey data. Compared
with the internal working speed of a computer, the rate of
operation of its peripheral equipment—in particular, the
input and output mechanisms—is slow. (Simpson)
1961
10.
1966
Computer matching studies. Despite the evidence
on the romantic nature of women ... the present data
indicate that in a first dating situation, men more often
than women experience romantic attraction for their
partners. (Coombs & Kenkel, J. Marriage & Family)
Computers can analyze survey data. Compared
with the internal working speed of a computer, the rate of
operation of its peripheral equipment—in particular, the
input and output mechanisms—is slow. (Simpson)
1961
11.
1966
Computer matching studies. Despite the evidence
on the romantic nature of women ... the present data
indicate that in a first dating situation, men more often
than women experience romantic attraction for their
partners. (Coombs & Kenkel, J. Marriage & Family)
Computers can analyze survey data. Compared
with the internal working speed of a computer, the rate of
operation of its peripheral equipment—in particular, the
input and output mechanisms—is slow. (Simpson)
1961
1970 The information age /
information explosion. (Toffler)
12.
1966
Computer matching studies. Despite the evidence
on the romantic nature of women ... the present data
indicate that in a first dating situation, men more often
than women experience romantic attraction for their
partners. (Coombs & Kenkel, J. Marriage & Family)
Computer communication democratize
information. Death of distance. Scientists
in obscure universities ... will be able to participate
in scientific discourse more readily. (Folk)
1977
Computers can analyze survey data. Compared
with the internal working speed of a computer, the rate of
operation of its peripheral equipment—in particular, the
input and output mechanisms—is slow. (Simpson)
1961
1970 The information age /
information explosion. (Toffler)
1967-77
Golden age of social network analysis.
Small-world experiment. Centrality indices.
Similarity indices. Strength of weak ties.
13.
Computer communication & society.
As more and more people use computer-mediated
communication, its societal effects are becoming
critical research topics. (Kiesler, McGuire)
1984
Linguistic changes might occur as a result of
computer mediated communication. (Baron)1984
Internet Relay Chat Open chat-rooms.
Net.romances was a designated dating channel1988
Prostitution advertised on Usenet groups
alt.sex.services, later alt.sex.prostitution1986
Communicating emotions electronically. Is
electronic communication depersonalizing? … Communicators
must imagine their audience, for at a terminal it almost seems as
though the computer itself is the audience. (Kiesler, McGuire)
1982
14.
1999
Scale-free networks.A large class of networks have
power-law degree distributions. Triggered search for
universal features & mechanisms (Barabási & Albert).
WWW dating sites. match.com1995
First scholarly work on IRCAnonymity make love-
seekers braver. Special netiquette develops. Internet
communication as a data source about social interaction. (Reid)
1991
Datamining. Ways to find patterns in
data beyond regression.late 1980’s
Data driven social network studies.
E-small-world, bursty dynamics, crowd intelligence2000’s
2003 Holme, Edling, Liljeros, Structure and time-evolution of an
Internet dating community, Social Networks 26:155–174.
Rocha, Liljeros, Holme, Information dynamics
shape the sexual networks of Internet-mediated
prostitution, PNAS 107:5706–5711.
2010
16.
P.Holme,C.R.Edling&F.Liljeros.
Structureandtime-evolutionofan
Internetdatingcommunity.
SocialNetworks26:155–174,2004.
You are logged in as:
user Z P20
You have one new message
Message box
Hey you Friday, July 5, 200
User A F20
Here User A has
space to write
about herself . . .
» Community /user A F
all in one place N
u
21.
a
Cumulativedistribution,P(k)
Number of partners, k
Females
Males
α
10��
10��
10��
10�
10��
10�10�10�
22.
bCumulativedistribution,P(ktot)
Total number of partners, k
totα
Females
Males
10�
10��
10��
10��
10�
10��
� 10�10�10�
23.
Liljeros et al., 2001.
The Web of Human
Sexual Contacts Nature
411:908–909.
ba
Cumulativedistribution,P(k)
Cumulativedistribution,P(ktot)
Number of partners, k Total number of partners, ktot
totα
Females
Males
Females
Males
α
10
10��
10��
10��
10�
10��
10��
10��
10��
10�
10��
10�10�10� 10�10�10�
27.
Who buys sex & why?
Pitts et al., 2004.Arch. Sex. Behav. 33:353–358.
28.
Cultural differences
Wikipedia
legal and
regulated
legal but pimping,
brothels, etc. are illegal illegal unknown
29.
Peculiar economics
Edlund et al., 2009. “The Wages of sin” working paper.
30.
What determines the price?
Increasing the number of prostitutes by one adds to the totalp (i)Fm ipn
expenditure on prostitutes. If then the revenue per∗
p (i)F ! p (n)F ,m ipn npi
prostitute must fall; conversely, if the revenue must∗
p (i)F 1 p (n)F ,m ipn npi
increase. Formally,
∗
! 0 if p (i)F ! p (n)Fm ipn npi
∗′ ∗
p (n)F p 0 if p (i)F p p (n)F (13)npi m ipn npi
{ ∗
1 0 if p (i)F 1 p (n)F .m ipn npi
Condition (13) implies that there is a unique if∗
n (0, N )
and To see this, note that∗ ∗
p (0) 1 p (0) ϩ w(0) p (N ) ! p (N ) ϩ w(N ).m m
then and cross at most once since and∗ ′
p (n)F p (i) p (i) 1 0npi m m
if∗′ ∗
p (n)F p 0 p (i) p p (n)F .npi m npi
As before, if N is sufficiently large. Moreover,∗
p (0) 1 p (0) ϩ w(0)m
∗
p (N ) ! p (N ) (14)m
is a sufficient condition for To see that condition∗
p (N ) ! p (N ) ϩ w(N ).m
(14) holds, note that if the richest man buys the services fromn p N,
more than one prostitute; there are no wives, and Hence,∗
ˆp (N ) ! y(N ).
he would be willing to pay to one woman to be his wife∗
p (N ) 1 p (N )m
instead of his full-time prostitute. Q.E.D.
Edlund, Korn, 2002. A theory of
prostitution, J. Pol. Econ. 110:181–
214.
31.
How is the information shared between sex-buyers.
What is the relation between different types of
prostitution?
What are the trends? Can we project into the future?
What are the implications for disease spreading?
What determines the price?
Human dynamics.
32.
Rocha, Liljeros, Holme, 2010. PNAS 107, 5706-5711.
Elizabeth Pisani, http://www.wisdomofwhores.com/
Swedes make sex boring, even in Brazil
Another thumbs down for the Swedish model. Not
the leggy blonde, not even Sweden’s moralistic
approach to the sex trade. This one is the Swedish
research model, which has managed to turn the
fascinating subject of on-line rating of hookers by
Brazilian punters into something indescribably dull.
33.
Rocha, Liljeros, Holme, 2010. PNAS 107, 5706-5711.
Elizabeth Pisani, http://www.wisdomofwhores.com/
Swedes make sex boring, even in Brazil
Another thumbs down for the Swedish model. Not
the leggy blonde, not even Sweden’s moralistic
approach to the sex trade. This one is the Swedish
research model, which has managed to turn the
fascinating subject of on-line rating of hookers by
Brazilian punters into something indescribably dull.
38.
Metrics Buyers Sellers
Number of vertices 10,106 6,624
Size of largest component 9,652 6,158
Number of edges 40,895
Number of encounters 50,185
Original Randomized
Diameter of largest comp. 17 13.2 ± 0.1
Average distance 5.78 4.921 ± 0.002
Number of 4-cycles 231,439 64,360 ± 302
Assortativity −0.110 −0.0896 ± 0.0005
40.
Degree distribution &
preferential attachment
1
1 10
K
1 10
K
1
p(k≥K)
p(k≥K)
0.1 1
sampling time
0.9
1
1.1
δ
0.9
1
1.1
δ
sampling time
0.1 1
sellers buyers
10�� 10��
10��10��
0.1
10��
0.1
10��
10� 10�
41.
0.6
0.7
0.8
0.9
1.0
frequencyofnocondomuse
0.7
0.8
0.9
1.0
10 100
post number, τ post number, τ
1 10 100 1
frequencyofnocondomuse
sellers buyers
Trends in risky behavior
42.
1
10
10
F
1
10
10
F
T (days)
~ T
0.5
~ T
0.5
T (days)
sellers buyers
~ T
1.2
~ T
0.6
~ T
0.6
~ T
1.2
10� 10� 10� 10�
10��10��
0.1 0.1
Detrended fluctuation analysis