Social Networks

1
Social Networks
And their applications to Web

• First half based on slides by
• Kentaro Toyama,
• Microsoft Research, India

2
NetworksPhysical Cyber
Typhoid Mary (Mary Mallon)
Patient Zero (Gaetan Dugas)
3
Applications of Network Theory

• World Wide Web and hyperlink structure
• The Internet and router connectivity
• Collaborations among
• Movie actors
• Scientists and mathematicians
• Sexual interaction
• Cellular networks in biology
• Food webs in ecology
• Phone call patterns
• Word co-occurrence in text
• Neural network connectivity of flatworms
• Conformational states in protein folding

4
Society as a Graph
People are represented as nodes.
5
Society as a Graph
People are represented as nodes. Relationships
are represented as edges. (Relationships may be
acquaintanceship, friendship, co-authorship,
etc.)
6
Society as a Graph
People are represented as nodes. Relationships
are represented as edges. (Relationships may be
acquaintanceship, friendship, co-authorship,
etc.) Allows analysis using tools of
mathematical graph theory
7
History (based on Freeman, 2000)

• 17th century Spinoza developed first model
• 1937 J.L. Moreno introduced sociometry he also
  invented the sociogram
• 1948 A. Bavelas founded the group networks
  laboratory at MIT he also specified centrality

8
History (based on Freeman, 2000)

• 1949 A. Rapaport developed a probability based
  model of information flow
• 50s and 60s Distinct research by individual
  researchers
• 70s Field of social network analysis emerged.
• New features in graph theory more general
  structural models
• Better computer power analysis of complex
  relational data sets

9
Graphs Sociograms (based on Hanneman, 2001)

• Strength of ties
• Nominal
• Signed
• Ordinal
• Valued

10
Visualization Software Krackplot
Sources http//www.andrew.cmu.edu/user/krack/kra
ckplot/mitch-circle.html http//www.andrew.cmu.ed
u/user/krack/krackplot/mitch-anneal.html
11
Connections

• Size  
• Number of nodes
• Density
• Number of ties that are present the amount of
  ties that could be present
• Out-degree
• Sum of connections from an actor to others
• In-degree
• Sum of connections to an actor

12
Distance

• Walk
• A sequence of actors and relations that begins
  and ends with actors
• Geodesic distance
• The number of relations in the shortest possible
  walk from one actor to another
• Maximum flow
• The amount of different actors in the
  neighborhood of a source that lead to pathways to
  a target

13
Some Measures of Power Prestige(based on
Hanneman, 2001)

• Degree
• Sum of connections from or to an actor
• Closeness centrality
• Distance of one actor to all others in the
  network
• Betweenness centrality
• Number that represents how frequently an actor is
  between other actors geodesic paths
• and when you have a DAG
• Authority-ness and Hub-ness
• Page-rank

14
Cliques and Social Roles (based on Hanneman,
2001)

• Cliques
• Sub-set of actors
• More closely tied to each other than to actors
  who are not part of the sub-set
• (A lot of work on trawling for communities in
  the web-graph)
• Often, you first find the clique (or a densely
  connected subgraph) and then try to interpret
  what the clique is about
• Social roles
• Defined by regularities in the patterns of
  relations among actors

15
Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Searchable Networks
• Six Degrees of Separation

16
Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Searchable Networks
• Six Degrees of Separation

17
Trying to make friends
Kentaro
18
Trying to make friends
Bash
Microsoft
Kentaro
19
Trying to make friends
Bash
Microsoft
Asha
Kentaro
Ranjeet
20
Trying to make friends
Bash
Microsoft
Asha
Kentaro
Ranjeet
Sharad
Yale
New York City
Ranjeet and I already had a friend in common!
21
I didnt have to worry
Bash
Kentaro
Sharad
Anandan
Venkie
Karishma
Maithreyi
Soumya
22
Its a small world after all!
Rao
Bash
Kentaro
Ranjeet
Sharad
Prof. McDermott
Anandan
Prof. Sastry
Prof. Veni
Prof. Kannan
Prof. Balki
Venkie
Ravis Father
Karishma
Ravi
Pres. Kalam
Prof. Prahalad
Pawan
Maithreyi
Prof. Jhunjhunwala
Aishwarya
Soumya
PM Manmohan Singh
Dr. Isher Judge Ahluwalia
Amitabh Bachchan
Dr. Montek Singh Ahluwalia
Nandana Sen
Prof. Amartya Sen
23
The Kevin Bacon Game

• Invented by Albright College students in 1994
• Craig Fass, Brian Turtle, Mike Ginelly
• Goal Connect any actor to Kevin Bacon, by
  linking actors who have acted in the same movie.
• Oracle of Bacon website uses Internet Movie
  Database (IMDB.com) to find shortest link between
  any two actors
• http//oracleofbacon.org/

Boxed version of the Kevin Bacon Game
24
The Kevin Bacon Game
An Example

• Kevin Bacon

Mystic River (2003)
Tim Robbins
Code 46 (2003)
Om Puri
Yuva (2004)
Rani Mukherjee
Black (2005)
Amitabh Bachchan
25
actually Bachchan has a Bacon number 3

• Perhaps the other path is deemed more diverse/
  colorful

26
The Kevin Bacon Game

• Total of actors in database 550,000
• Average path length to Kevin 2.79
• Actor closest to center Rod Steiger (2.53)
• Rank of Kevin, in closeness to center 876th
• Most actors are within three links of each other!

Center of Hollywood?
27
Not Quite the Kevin Bacon Game

• Kevin Bacon

Cavedweller (2004)
Aidan Quinn
Looking for Richard (1996)
Kevin Spacey
Bringing Down the House (2004)
Ben Mezrich
Roommates in college (1991)
Kentaro Toyama
28
Erdos Number (Bacon game for Brainiacs ? )

• Number of links required to connect scholars to
  Erdos, via co-authorship of papers
• Erdos wrote 1500 papers with 507 co-authors.
• Jerry Grossmans (Oakland Univ.) website allows
  mathematicians to compute their Erdos numbers
• http//www.oakland.edu/enp/
• Connecting path lengths, among mathematicians
  only
• average is 4.65
• maximum is 13

Paul Erdos (1913-1996)
Unlike Bacon, Erdos has better centrality in his
network
29
Erdos Number
An Example

• Paul Erdos

Alon, N., P. Erdos, D. Gunderson and M. Molloy
(2002). On a Ramsey-type Problem. J. Graph Th.
40, 120-129.
Mike Molloy
Achlioptas, D. and M. Molloy (1999). Almost All
Graphs with 2.522 n Edges are not 3-Colourable.
Electronic J. Comb. (6), R29.
Dimitris Achlioptas
Achlioptas, D., F. McSherry and B. Schoelkopf.
Sampling Techniques for Kernel Methods. NIPS
2001, pages 335-342.
Bernard Schoelkopf
Romdhani, S., P. Torr, B. Schoelkopf, and A.
Blake (2001). Computationally efficient face
detection. In Proc. Intl. Conf. Computer Vision,
pp. 695-700.
Andrew Blake
Toyama, K. and A. Blake (2002). Probabilistic
tracking with exemplars in a metric space.
International Journal of Computer Vision.
48(1)9-19.
Kentaro Toyama
30
..and Rao has even shorter distance ?
31
..collaboration distances
32
Six Degrees of Separation
Milgram (1967)

• The experiment
• Random people from Nebraska were to send a letter
  (via intermediaries) to a stock broker in Boston.
• Could only send to someone with whom they were on
  a first-name basis.
• Among the letters that found the target, the
  average number of links was six.

Stanley Milgram (1933-1984)
33
Six Degrees of Separation
Milgram (1967)

• John Guare wrote a play called Six Degrees of
  Separation, based on this concept.

Everybody on this planet is separated by only
six other people. Six degrees of separation.
Between us and everybody else on this planet. The
president of the United States. A gondolier in
Venice Its not just the big names. Its anyone.
A native in a rain forest. A Tierra del Fuegan.
An Eskimo. I am bound to everyone on this planet
by a trail of six people
34
Outline

• Small Worlds
• Random Graphs--- Or why does the small world
  phenomena exist?
• Alpha and Beta
• Power Laws
• Searchable Networks
• Six Degrees of Separation

35
Random Graphs
N 12
Erdos and Renyi (1959)
p 0.0 k 0

• N nodes
• A pair of nodes has probability p of being
  connected.
• Average degree, k pN
• What interesting things can be said for different
  values of p or k ?
• (that are true as N ? 8)

p 0.09 k 1
p 1.0 k ½N2
36
Random Graphs
Erdos and Renyi (1959)
p 0.0 k 0
p 0.09 k 1
p 0.045 k 0.5
Lets look at
Size of the largest connected cluster
p 1.0 k ½N2
Diameter (maximum path length between nodes) of
the largest cluster
Average path length between nodes (if a path
exists)
37
Random Graphs
Erdos and Renyi (1959)
p 0.0 k 0
p 0.09 k 1
p 1.0 k ½N2
p 0.045 k 0.5
Size of largest component
1
5
11
12
Diameter of largest component
4
0
7
1
Average path length between (connected) nodes
0.0
2.0
1.0
4.2
38
Random Graphs
Erdos and Renyi (1959)
Percentage of nodes in largest component Diameter
of largest component (not to scale)

• If k lt 1
• small, isolated clusters
• small diameters
• short path lengths
• At k 1
• a giant component appears
• diameter peaks
• path lengths are high
• For k gt 1
• almost all nodes connected
• diameter shrinks
• path lengths shorten

1.0
0
1.0
k
phase transition
39
Random Graphs
Erdos and Renyi (1959)

• What does this mean?
• If connections between people can be modeled as a
  random graph, then
• Because the average person easily knows more than
  one person (k gtgt 1),
• We live in a small world where within a few
  links, we are connected to anyone in the world.
• Erdos and Renyi showed that average
• path length between connected nodes is

40
Random Graphs
Erdos and Renyi (1959)

• What does this mean?
• If connections between people can be modeled as a
  random graph, then
• Because the average person easily knows more than
  one person (k gtgt 1),
• We live in a small world where within a few
  links, we are connected to anyone in the world.
• Erdos and Renyi computed average
• path length between connected nodes to be

41
Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws ---and scale-free networks
• Searchable Networks
• Six Degrees of Separation

42
Random vs. Real Social networks

• Real networks are not exactly like these
• Tend to have a relatively few nodes of high
  connectivity (the Hub nodes)
• These networks are called Scale-free networks

• Random network models introduce an edge between
  any pair of vertices with a probability p
• The problem here is NOT randomness, but rather
  the distribution used (which, in this case, is
  uniform)

43
Power Laws
Albert and Barabasi (1999)

• Whats the degree (number of edges) distribution
  over a graph, for real-world graphs?
• Random-graph model results in Poisson
  distribution.
• But, many real-world networks exhibit a power-law
  distribution.

Degree distribution of a random graph, N 10,000
p 0.0015 k 15. (Curve is a Poisson curve,
for comparison.)
44
Power Laws
Albert and Barabasi (1999)

• Whats the degree (number of edges) distribution
  over a graph, for real-world graphs?
• Random-graph model results in Poisson
  distribution.
• But, many real-world networks exhibit a power-law
  distribution.

k-r (2lt r lt 3)
Typical shape of a power-law distribution.
For web graph r 2.1 for in degree
distribution 2.7 for out degree distribution
45
11/30 Class to start here

• --Report due in class on Monday
• --Structure of next class

46
Power Laws
Albert and Barabasi (1999)

• Power-law distributions are straight lines in
  log-log space.
• How should random graphs be generated to create a
  power-law distribution of node degrees?
• Hint
• Paretos Law Wealth distribution follows a
  power law.

Power laws in real networks (a) WWW
hyperlinks (b) co-starring in movies (c)
co-authorship of physicists (d) co-authorship of
neuroscientists
Same Velfredo Pareto, who defined Pareto
optimality in game theory.
47
Power Laws Scale-Free Networks

• The rich get richer!
• Power-law distribution of node-degree arises if
• (but not only if)
• Number of nodes grow
• Edges are added in proportion to the number of
  edges a node already has.
• Alternative Copy modelwhere the new node copies
  a random subset of the links of an existing node
• Sort of close to the WEB reality

• Examples of Scale-free networks (i.e., those that
  exhibit power law distribution of in degree)
• Social networks, including collaboration
  networks. An example that have been studied
  extensively is the collaboration of movie actors
  in films.
• Protein-interaction networks.
• Sexual partners in humans, which affects the
  dispersal of sexually transmitted diseases.
• Many kinds of computer networks, including the
  World Wide Web.

48
Scale-free Networks

• Scale-free networks also exhibit small-world
  phenomena
• For a random graph having the same power law
  distribution as the Web graph, it has been shown
  that
• Avg path length 0.35 log10 N
• However, scale-free networks tend to be more
  brittle
• You can drastically reduce the connectivity by
  deliberately taking out a few nodes
• This can also be seen as an opportunity..
• Disease prevention by quarantaining
  super-spreaders
• As they actually did to poor Typhoid Mary..

49
Attacks vs. Disruptionson Scale-free vs. Random
networks

• Disruption
• A random percentage of the nodes are removed
• How does the diameter change?
• Increases monotonically and linearly in random
  graphs
• Remains almost the same in scale-free networks
• Since a random sample is unlikely to pick the
  high-degree nodes

• Attack
• A precentage of nodes are removed willfully (e.g.
  in decreasing order of connectivity)
• How does the diameter change?
• For random networks, essentially no difference
  from disruption
• All nodes are approximately same
• For scale-free networks, diameter doubles for
  every 5 node removal!
• This is an opportunity when you are fighting to
  contain spread

50
Exploiting/Navigating Small-Worlds
How does a node in a social network find a path
to another node? ? 6 degrees of separation
will lead to n6 search space (nnum neighbors)
?Easy if we have global graph.. But
hard otherwise

• Case 2 Local access to network structure
• Each node only knows its own neighborhood
• Search without children-generation function ?
• Idea 1 Broadcast method
• Obviously crazy as it increases traffic
  everywhere
• Idea 2 Directed search
• But which neighbors to select?
• Are there conditions under which decentralized
  search can still be easy?

• Case 1 Centralized access to network structure
• Paths between nodes can be computed by shortest
  path algorithms
• E.g. All pairs shortest path
• ..so, small-world ness is trivial to exploit..
• This is what ORKUT, Friendster etc are trying to
  do..

There are very few fully decentralized search
applications. You normally have hybrid
methods between Case 1 and Case 2
Computing ones Erdos number used to take days in
the past!
51
Searchability in Small World Networks

• Searchability is measured in terms of Expected
  time to go from a random source to a random
  destination
• We know that in Smallworld networks, the diameter
  is exponentially smaller than the size of the
  network.
• If the expected time is proportional to some
  small power of log N, we are doing well
• Qn Is this always the case in small world
  networks?
• To begin to answer this we need to look
  generative models that take a notion of absolute
  (lattice or coordinate-based) neighborhood into
  account
• Kleinberg experimented with Lattice networks
  (where the network is embedded in a latticewith
  most connections to the lattice neighbors, but a
  few shortcuts to distant neighbors)
• and found that the answer is Not always

Kleinberg (2000)
52
Neighborhood based random networks

• Lattice is d-dimensional (d2).
• One random link per node.
• Probability that there is a link between two
  nodes u and v is r(u,v)- a
• r(u,v) is the lattice distance between u and v
  (computed as manhattan distance)
• As against geodesic or network distance computed
  in terms of number of edges
• E.g. North-Rim and South-Rim
• - a determines how steeply the probability of
  links to far away neighbors reduces

View of the world from 9th Ave
53
Searcheability inlattice networks

• For d2, dip in time-to-search at a2
• For low a, random graph no geographic
  correlation in links
• For high a, not a small world no short paths to
  be found.
• Searcheability dips at a2 (inverse square
  distribution), in simulation
• Corresponds to using greedy heuristic of sending
  message to the node with the least lattice
  distance to goal
• For d-dimensional lattice, minimum occurs at ad

54
Searchable Networks
Kleinberg (2000)

• Watts, Dodds, Newman (2002) show that for d 2
  or 3, real networks are quite searchable.
• ?the dimensions are things like
  geography, profession, hobbies
• Killworth and Bernard (1978) found that people
  tended to search their networks by d 2
  geography and profession.

The Watts-Dodds-Newman model closely fitting a
real-world experiment
55
..but didnt Milgrams letter experiment show
that navigation is easy?

• may be not
• A large fraction of his test subjects were
  stockbrokers
• So are likely to know how to reach the goal
  stockbroker
• A large fraction of his test subjects were in
  boston
• As was the goal stockbroker
• A large fraction of letters never reached
• Only 20 reached
• So how about (re)doing Milgram experiment with
  emails?
• People are even more burned out with (e)mails now
• Success rate for chain completion lt 1 !

56
Summary

• A network is considered to exhibit small world
  phenomenon, if its diameter is approximately
  logarithm of its size (in terms of number of
  nodes)
• Most uniform random networks exhibit small world
  phenomena
• Most real world networks are not uniform random
• Their in degree distribution exhibits power law
  behavior
• However, most power law random networks also
  exhibit small world phenomena
• But they are brittle against attack
• The fact that a network exhibits small world
  phenomenon doesnt mean that an agent with
  strictly local knowledge can efficiently navigate
  it (i.e, find paths that are O(log(n)) length
• It is always possible to find the short paths if
  we have global knowledge
• This is the case in the FOAF (friend of a friend)
  networks on the web

57
Web Applications of Social Networks

• Analyzing page importance
• Page Rank
• Related to recursive in-degree computation
• Authorities/Hubs
• Discovering Communities
• Finding near-cliques
• Analyzing Trust
• Propagating Trust
• Using propagated trust to fight spam
• In Email
• In Web page ranking

58
Spam is a serious problem

• We have Spam Spam Spam Spam Spam with Eggs and
  Spam
• in Email
• Most mail transmitted is junk
• web pages
• Many different ways of fooling search engines
• This is an open arms race
• Annual conference on Email and Anti-Spam
• Started 2004
• Intl. workshop on AIR-Web (Adversarial Info
  Retrieval on Web)
• Started in 2005 at WWW

59
Trust Spam (Knock-Knock. Who is there?)

• A powerful way we avoid spam in our physical
  world is by preferring interactions only with
  trusted parties
• Trust is propagated over social networks
• When knocking on the doors of strangers, the
  first thing we do is to identify ourselves as a
  friend of a friend of friend
• So they wont train their dogs/guns on us..
• Knock-knock. Who is there? Aardwark. Okay (door
  opened) ?not funny
• Aardwark who? Aardwark a million miles for one of
  your smiles. ?FUNNY
• We can do it in cyber world too
• Accept product recommendations only from trusted
  parties
• E.g. Epinions
• Accept mails only from individuals who you trust
  above a certain threshold
• Bias page importance computation so that it
  counts only links from trusted sites..
• Sort of like discounting links that are off
  topic

60
Trust Propagation

• Trust is transitive so easy to propagate
• ..but attenuates as it traverses as a social
  network
• If I trust you, I trust your friend (but a little
  less than I do you), and I trust your friends
  friend even less
• Trust may not be symmetric..
• Trust is normally additive
• If you are friend of two of my friends, may be I
  trust you more..
• Distrust is difficult to propagate
• If my friend distrusts you, then I probably
  distrust you
• but if my enemy distrusts you?
• is the enemy of my enemy automatically my
  friend?
• Trust vs. Reputation
• Trust is a user-specific metric
• Your trust in an individual may be different from
  someone elses
• Reputation can be thought of as an aggregate
  or one-size-fits-all version of Trust
• Most systems such as EBay tend to use Reputation
  rather than Trust
• Sort of the difference between User-specific vs.
  Global page rank

61
Case Study Epinions

• Users can write reviews and also express
  trust/distrust on other users
• Reviewers get royalties
• so some tried to game the system
• So, distrust measures introduced

Num nodes
Out degree
Guha et. Al. WWW 2004 compares some 81
different ways of propagating trust and
distrust on the Epinion trust matrix
62
Evaluating Trust Propagation Approaches

• Given n users, and a sparsely populated nxn
  matrix of trusts between the users
• And optionally an nxn matrix of distrusts between
  the users
• Start by erasing some of the entries (but
  remember the values you erased)
• For each trust propagation method
• Use it to fill the nxn matrix
• Compare the predicted values to the erased values

63
Fighting Page Spam
We saw discussion of these in the Henzinger et.
Al. paper
Can social networks, which gave rise to the
ideas of page importance computation, also
rescue these computations from spam?
64
TrustRank idea
Gyongyi et al, VLDB 2004

• Tweak the default distribution used in page
  rank computation (the distribution that a bored
  user uses when she doesnt want to follow the
  links)
• From uniform
• To Trust based
• Very similar in spirit to the Topic-sensitive or
  User-sensitive page rank
• Where too you fiddle with the default
  distribution
• Sample a set of seed pages from the web
• Have an oracle (human) identify the good pages
  and the spam pages in the seed set
• Expensive task, so must make seed set as small as
  possible
• Propagate Trust (one pass)
• Use the normalized trust to set the initial
  distribution

Slides modified from Anand Rajaramans lecture at
Stanford
65
Example
1
2
3
good
4
bad
5
6
7
66
Rules for trust propagation

• Trust attenuation
• The degree of trust conferred by a trusted page
  decreases with distance
• Trust splitting
• The larger the number of outlinks from a page,
  the less scrutiny the page author gives each
  outlink
• Trust is split across outlinks
• Combining splitting and damping, each out link of
  a node p gets a propagated trust of
  bt(p)/O(p)
• 0ltblt1 O(p) is the out degree and t(p) is the
  trust of p
• Trust additivity
• Propagated trust from different directions is
  added up

67
Simple model

• Suppose trust of page p is t(p)
• Set of outlinks O(p)
• For each q2O(p), p confers the trust
• bt(p)/O(p) for 0ltblt1
• Trust is additive
• Trust of p is the sum of the trust conferred on p
  by all its inlinked pages
• Note similarity to Topic-Specific Page Rank
• Within a scaling factor, trust rank biased page
  rank with trusted pages as teleport set

68
Picking the seed set

• Two conflicting considerations
• Human has to inspect each seed page, so seed set
  must be as small as possible
• Must ensure every good page gets adequate trust
  rank, so need make all good pages reachable from
  seed set by short paths

69
Approaches to picking seed set

• Suppose we want to pick a seed set of k pages
• PageRank
• Pick the top k pages by page rank
• Assume high page rank pages are close to other
  highly ranked pages
• We care more about high page rank good pages

70
Inverse page rank

• Pick the pages with the maximum number of
  outlinks
• Can make it recursive
• Pick pages that link to pages with many outlinks
• Formalize as inverse page rank
• Construct graph G by reversing each edge in web
  graph G
• Page Rank in G is inverse page rank in G
• Pick top k pages by inverse page rank

71
End of Lecture
72
Applications of Network Theory

• World Wide Web and hyperlink structure
• The Internet and router connectivity
• Collaborations among
• Movie actors
• Scientists and mathematicians
• Sexual interaction
• Cellular networks in biology
• Food webs in ecology
• Phone call patterns
• Word co-occurrence in text
• Neural network connectivity of flatworms
• Conformational states in protein folding

73
Credits
Albert, Reka and A.-L. Barabasi. Statistical
mechanics of complex networks. Reviews of Modern
Physics, 74(1)47-94. (2002) Barabasi,
Albert-Laszlo. Linked. Plume Publishing.
(2003) Kleinberg, Jon M. Navigation in a small
world. Science, 406845. (2000) Watts, Duncan.
Six Degrees The Science of a Connected Age. W.
W. Norton Co. (2003)
74
Six Degrees of Separation
Milgram (1967)

• The experiment
• Random people from Nebraska were to send a letter
  (via intermediaries) to a stock broker in Boston.
• Could only send to someone with whom they were on
  a first-name basis.
• Among the letters that found the target, the
  average number of links was six.

Stanley Milgram (1933-1984)
75
Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Searchable Networks
• Six Degrees of Separation

76
Neighborhood based generative models

• These essentially give more links to close
  neighbors..

77
The Alpha Model
Watts (1999)

• The people you know arent randomly chosen.
• People tend to get to know those who are two
  links away (Rapoport , 1957).
• The real world exhibits a lot of clustering.

The Personal Map by MSR Redmonds Social
Computing Group
Same Anatol Rapoport, known for TIT FOR TAT!
78
The Alpha Model
Watts (1999)

• a model Add edges to nodes, as in random
  graphs, but makes links more likely when two
  nodes have a common friend.
• For a range of a values
• The world is small (average path length is
  short), and
• Groups tend to form (high clustering
  coefficient).

Probability of linkage as a function of number of
mutual friends (a is 0 in upper left, 1 in
diagonal, and 8 in bottom right curves.)
79
The Alpha Model
Watts (1999)

• a model Add edges to nodes, as in random
  graphs, but makes links more likely when two
  nodes have a common friend.
• For a range of a values
• The world is small (average path length is
  short), and
• Groups tend to form (high clustering
  coefficient).

a
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The Beta Model
Watts and Strogatz (1998)
b 0
b 0.125
b 1
People know others at random. Not clustered, but
small world
People know their neighbors, and a few distant
people. Clustered and small world
People know their neighbors. Clustered,
but not a small world
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The Beta Model
Jonathan Donner
Kentaro Toyama
Watts and Strogatz (1998)
Nobuyuki Hanaki

• First five random links reduce the average path
  length of the network by half, regardless of N!
• Both a and b models reproduce short-path results
  of random graphs, but also allow for clustering.
• Small-world phenomena occur at threshold between
  order and chaos.

Clustering coefficient / Normalized path length
Clustering coefficient (C) and average path
length (L) plotted against b
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Searchable Networks
Kleinberg (2000)

• Just because a short path exists, doesnt mean
  you can easily find it.
• You dont know all of the people whom your
  friends know.
• Under what conditions is a network searchable?