overview definition: the clustering coefficient of a node v · definition: the clustering...
TRANSCRIPT
The Science of Networks 6.1
Overview Social Goal. Explain why information and disease
spread so quickly in social networks. Mathematical Approach. Model social networks as
random graphs and argue that they are likely to have low diameter.
The Science of Networks 6.2
Definition: The clustering coefficient of a node v is the fraction of pairs of v’s friends that are connected to each other by edges.
Clustering Coefficient = 1/2
The higher the clustering coefficient of a node, the more strongly triadic closure is acting on it
The Science of Networks 6.3
Erdos-Renyi Graph models ! Randomly choose ! How well does the E-R model characterize real
networks? ! high school friendship networks ! road networks ! peer-to-peer filesharing networks ! product co-purchase networks (people who bought X also
bought Y) ! other?
The Science of Networks 6.4
Random graph diameter Technique. Grow trees to bound path lengths.
1. Show when trees are small enough (< !n), # of leaves doubles.
2. Grow small trees (< !n) around a pair of nodes.
3. Use birthday paradox to argue trees prob. intersect.
Conclude diameter is 2 log !n = log n.
The Science of Networks 6.5
Small world phenomenon Milgram’s experiment (1960s).
Ask someone to pass a letter to another person via friends knowing only the name, address, and occupation of the target.
The Science of Networks 6.6
Small world phenomenon
Bob, a farmer in Nebraska
David, mayor of Bob’s town
Bernard, David’s cousin who went to college with
Maya, who grew up in Boston
With Lashawn
The Science of Networks 6.7
Last time: short paths exist. (argument: flood the network)
The Science of Networks 6.8
This time: and people can find short paths! (without flooding the network)
The Science of Networks 6.9
The Milgram experiment Experiment:
We choose a random “source” and “target”. Your goal: pass ball from “source” to “target” by throwing it to people you know on a first-name basis.
The Science of Networks 6.10
How did you find short paths?
The Science of Networks 6.11
What information did you use to choose the next hop?
The Science of Networks 6.12
Social network models
Random rolodex model
Random wiring model
The Science of Networks 6.13
Random wiring model
People have a predictable structure of local links (e.g., neighbors, colleagues)
And a few random long-range links (e.g., someone you meet on a trip)
The Science of Networks 6.14
Random wiring model Local links have grid-like structure (you know the person on your left/right and front/back)
n
n
The Science of Networks 6.15
Random wiring model Local links represent homophily, the idea that we know people similar to us
n
n The Science of Networks 6.16
Random wiring model Long-range links are random – each node chooses one long-range link
n
n
The Science of Networks 6.17
Random wiring model Long-range links represent weak ties, the links to acquantainces that would otherwise be far away
n
n The Science of Networks 6.18
Random wiring model
What do you expect the diameter to be?
Do you expect people to find short paths? If so, how short?
The Science of Networks 6.19
We’ll end up repeatedly over-shooting target.
What if long-range links are
uniformly random?
Short paths?
Problem: navigational clues lost in long-range links.
The Science of Networks 6.20
We’ll end up taking forever to
get anywhere.
What if long-range links are very localized?
Short paths?
Problem: increases path-length.
The Science of Networks 6.21
Decentralized search Idea: Suppose long-range links are just slightly more likely to be to close nodes.
Result: Then decentralized search finds short paths.
The Science of Networks 6.22
Tradeoff
Discovered path length
uniform
path length
Actual path length
highly local
The Science of Networks 6.23
Optimal tradeoff
Suppose links are proportional to (1/distance)2,
i.e., inverse square.
Inverse square?? WTF?
The Science of Networks 6.24
Inverse square intuition
“Scales of resolution”:
LSRC D235 450 Research Dr. Durham, NC USA
Room number Building
Street City State
Country
The Science of Networks 6.25
Scales of resolution
Street: location within 2 miles
The Science of Networks 6.26
Scales of resolution
City: location within 4 miles
The Science of Networks 6.27
Scales of resolution
County: location within 8 miles
The Science of Networks 6.28
Scales of resolution
Each new scale doubles distance from the center.
The Science of Networks 6.29
Scales of resolution
Long-range links equally likely to connect to each different scale of resolution!
(allows people to make progress towards destination no matter how far away they
are)
The Science of Networks 6.30
How many people do you know?
The Science of Networks 6.31
How many people do you know?
The Science of Networks 6.32
How many people do you know?
The Science of Networks 6.33
How many people do you know?
The Science of Networks 6.34
How many people do you know?
The Science of Networks 6.35
How many people do you know?
The Science of Networks 6.36
How many people do you know?
The Science of Networks 6.37
How well did this work? For me:
Neighborhood (Trinity Heights) Region (RTP) State (NC) Southeast Eastern US United States World
The Science of Networks 6.38
Next topic
decentralized search
The Science of Networks 6.39
How to route
Problem. How can I get this message from me to the far-away target?
Solution. Pass message to a friend.
closer
(sub)
The Science of Networks 6.40
Scales of resolution
Each new scale doubles distance from the center.
The Science of Networks 6.41
Long-range links
Suppose each person has a long-range friend in each
scale of resolution.
The Science of Networks 6.42
How to route
Algorithm. Pass the message to your farthest friend that is to the left of the target.
The Science of Networks 6.43
Trace of route
The Science of Networks 6.44
Analysis
old dist.
1 2 4 2j 2j+1
new dist.
The Science of Networks 6.45
Distance is cut in half every step!
The Science of Networks 6.46
Analysis 1. Original distance is ? 2. Distance is cut in half every step (at least). 3. Number of steps is ?
at most n.
at most log n.
The Science of Networks 6.47
And in real life …
The Science of Networks 6.48
Finding the Short Paths ! Milgram’s experiment, Columbia Small Worlds, E-R, !-
model… ! all emphasize existence of short paths between pairs
! How do individuals find short paths? ! in an incremental, next-step fashion ! using purely local information about the NW and location of target ! note: shortest path might require taking steps “away” from the
target! ! This is not (only) a structural question, but an algorithmic
one ! statics vs. dynamics
! Navigability may impose additional restrictions on formation model!
! Briefly investigate two alternatives: ! a local/long-distance mixture model [Kleinberg] ! a “social identity” model [Watts, Dodd, Newman]
The Science of Networks 6.49
Kleinberg’s Model ! Start with an n by n grid of vertices (so N = n^2)
! add some long-distance connections to each vertex: • k additional connections • probability of connection to grid distance d: ~ (1/d)^r
– c.f. dollar bill migration paper
! so full model given by choice of k and r ! large r: heavy bias towards “more local” long-distance connections ! small r: approach uniformly random
! Kleinberg’s question: ! what value of r permits effective navigation? ! # hops << N, e.g. log(N)
! Assume parties know only: ! grid address of target ! addresses of their own immediate neighbors
! Algorithm: pass message to nbr closest to target in grid
The Science of Networks 6.50
Kleinberg’s Result ! Intuition:
! if r is too large (strong local bias), then “long-distance” connections never help much; short paths may not even exist (remember, grid has large diameter, ~ sqrt(N))
! if r is too small (no local bias), we may quickly get close to the target; but then we’ll have to use grid links to finish
• think of a transport system with only long-haul jets or donkey carts ! effective search requires a delicate mixture of link distances
! The result (informally): ! r = 2 is the only value that permits rapid navigation (~log(N) steps) ! any other value of r will result in time ~ N^c for 0 < c <= 1
• N^c >> log(N) for large N ! a critical value phenomenon or “knife’s edge”; very sensitive
! Note: locality of information crucial to this argument ! centralized, “birds-eye” algorithm can still compute short paths at r <
2! ! can recognize when “backwards” steps are beneficial
The Science of Networks 6.51
Navigation via Identity ! Watts et al.:
! we don’t navigate social networks by purely “geographic” information
! we don’t use any single criterion; recall Dodds et al. on Columbia SW ! different criteria used at different points in the chain
! Represent individuals by a vector of attributes ! profession, religion, hobbies, education, background, etc… ! attribute values have distances between them (tree-structured) ! distance between individuals: minimum distance in any attribute ! only need one thing in common to be close!
! Algorithm: ! given attribute vector of target ! forward message to neighbor closest to target
! Permits fast navigation under broad conditions ! not as sensitive as Kleinberg’s model
all jobs
scientists athletes
chemistry CS
soccer track