This document discusses social network sites (SNS). It begins by defining SNS and noting their growth since the 1990s. It then reviews the history and concepts of social networks, small world networks, and online social networks. The document outlines several prominent SNS including Google's Orkut and various Chinese SNS. It concludes by suggesting that SNS have become important online social platforms and will continue evolving in the future.

1. SNS

2. o
o
o
o
“ ”
o

3. o
o 1.4
o

4. SNS
p
p SNS
p SNS
p

5. ü 1
ü 2
ü 3
ü 4
ü 5
ü 6
ü 7
ü 8 CSSN

6. 1

7. 1
Roger Brown
ü "Social structure becomes actually visible in an
anthill; the movements and contacts one sees are not
random but patterned. We should also be able to see
structure in the life of an American community if we
had a sufficiently remote vantage point, a point from
which persons would appear to be small moving
dots. . . . We should see that these dots do not
randomly approach one another, that some are
usually together, some meet often, some never. . . .
If one could get far enough away from it human life
would become pure pattern."

8. 2
o
o
o

9. 3
ü
-
-
ü
ü

10. 4
ü
ü
ü
ü
ü
wellman,1988

11. 5
p o
p o
p o
o
p
/
p Freeman o Whitte Lin
Burt

12. 5
o o
1
2
o
3
o 4 Van
der 1993
o

13. 6
o www.arcp.cn
o
o 010-66019664

14. 7
o INSNA
JSS JSN
o
o [ ]
[ ]
o

15. 8 CSSN
o CSSN Computer-
Supported Social Networks
o
o

16. SNS
l 2.1 SNS
l 2.2
l 2.3 1
l 2.4 2
l 2.5
l 2.6
l 2.7

17. 2.1 SNS
u 1 = 6
6
http://blog.donews.com/
hidy/archive/2006/03/12/764695.aspx
u 2 = + +…+
SNS
http://atblog.org/blog/index.php/2006_04_17_172.html

18. 2.2
l Milgram, Stanley. 1967. “The Small World Problem.”
Psychology Today 2:60–67.
l 1967 Stanley Milgram
(1933-1984)
5
6 1967 5
“ ”

19. 2.2
o Nature. 1998 Jun 4;393(6684):440-2.
o Collective dynamics of 'small-world' networks.
Watts DJ, Strogatz SH.
Department of Theoretical and Applied Mechanics, Cornell University, Ithaca, New York
14853, USA. djw24@columbia.edu
Networks of coupled dynamical systems have been used to model biological oscillators,
Josephson junction arrays, excitable media, neural networks, spatial games, genetic
control networks and many other self-organizing systems. Ordinarily, the connection
topology is assumed to be either completely regular or completely random. But many
biological, technological and social networks lie somewhere between these two
extremes. Here we explore simple models of networks that can be tuned through this
middle ground: regular networks 'rewired' to introduce increasing amounts of disorder.
We find that these systems can be highly clustered, like regular lattices, yet have small
characteristic path lengths, like random graphs. We call them 'small-world' networks,
by analogy with the small-world phenomenon (popularly known as six degrees of
separation. The neural network of the worm Caenorhabditis elegans, the power grid of
the western United States, and the collaboration graph of film actors are shown to be
small-world networks. Models of dynamical systems with small-world coupling display
enhanced signal-propagation speed, computational power, and synchronizability. In
particular, infectious diseases spread more easily in small-world networks than in
regular lattices.

20. 2.2
o AJS Volume 105, Number 2 (September 1999): 493–527 493
o Networks, Dynamics, and the Small-World Phenomenon1
Duncan J. Watts
Santa Fe Institute
The small-world phenomenon formalized in this article as the coincidence
of high local clustering and short global separation, is shown
to be a general feature of sparse, decentralized networks that are
neither completely ordered nor completely random. Networks of this
kind have received little attention, yet they appear to be widespread
in the social and natural sciences, as is indicated here by three distinct
examples. Furthermore, small admixtures of randomness to an
otherwise ordered network can have a dramatic impact on its dynamical,
as well as structural, properties—a feature illustrated by
a simple model of disease transmission.

21. 2.3
l 1

22. 2.4
l 2

23. 2.5
l & 2001
CSSN
l & 2004

25. 462 453 99 76 74 68 65 65 45 41
22. 22. 4.8 3.7 3.6 3.3 3.1 3.1 2.1 2.0
% 53 09 3 1 1 2 7 7 9 0
70.62%
3
297 23 120 96 90 70 69 60 36 40
1
16.6 12. 6.7 3.9 5.0 3.9 3.8 3.3 3.1 2.2
63.36% % 7 96 3 5 3 7 7 4 4
4

26. 2.6
l
1
2
n
n
n
3
1998& ,1999

27. 2.7
o
o
n —
n
o
n

28. SNS
Ø 3.1 Google SNS
Ø 3.2
Ø 3.3 SNS
Ø 3.4 SNS
Ø 3.5 SNS

29. 3.1 Google SNS
o Google +
o SNS
+

30. 3.2
o
o
o

31. 3.3 SNS
o
o
o

32. 3.4 SNS
o …
o
n Web Ajax+XML+XSLT
+JSP …
n
n

33. 3.5 SNS
o SNS
o SNS
o SNS
n Google
n SNS

34. o [1] . [J]. ,2001,48(4):
42-48.
o [2] , . [J]. ,2002(3):
56-58.
o [3] , . : CSSN [J]. . . ,2003
(01):51-55.
o [4] , , . [J].
,2003(01).
o [5] , , . [J]. ,2004(01).