Navigating with Power Laws Aaron Clauset CAIDA / SFI Networks and - - PowerPoint PPT Presentation
Navigating with Power Laws Aaron Clauset CAIDA / SFI Networks and - - PowerPoint PPT Presentation
Navigating with Power Laws Aaron Clauset CAIDA / SFI Networks and Navigability Working Group 8 August 2008 Milgram Study (1967) A question of social connectedness 60 letters sent to Wichita, Kansas Destination: wife of divinity stud.,
Milgram Study (1967)
A question of social connectedness
- 60 letters sent to Wichita, Kansas
- Destination: wife of divinity stud., Cambridge, Ma.
- Only 3 arrived
- Subsequent studies: mean path length ~6
Discoveries
- Surprisingly short paths; “small world” phenom.
- Shorts paths are locally discoverable
- S. Milgram, “The small world problem.” Psychology Today 2 (1967) 60--67.
Watts-Strogatz Model (1998)
- Modeled existence of short paths only
- diameter log(n)
D.J. Watts and S.H. Strogatz, “Collective dynamics of small-world networks.” Nature 393 (1998) 440-442.
Kleinberg Model (2000)
- Model of navigability/search
- Lattice + long range links
- (Manhattan) distance metric
- Local (greedy) navigation in ~ steps
log2(n)
- J. Kleinberg. “The small-world phenomenon: an algorithmic perspective.”
- Proc. 32nd ACM Symposium on Theory of Computing (2000) 163--170.
d(u, v) = |u − v|
An Aside: Finite Size Effects
- Simulate Kleinberg graphs with various
- Measure mean routing time
- Find severe finite size effects
e.g., for
- Keep this in mind for later
α ∼ d T αTopt(n) = 1 − A log2(n) d = 1 αTopt = d
Simulation Results
0.8 0.825 0.85 0.875 0.9 0.925 0.95 0.975 1 80 90 100 110 120 130 140
powerlaw exponent, α mean routing time, T αopt~0.900 n~2*105 αopt~0.911 n~5*105 αopt~0.921 n~1*106 αopt~0.928 n~2*106
Convergence
10
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10
2
10
3
10
4
10
5
10
6
10
7
10
−2
10
−1
10 network size, n 1 − αopt 1 − α(n) data 1 −2.8499log10
−2 (n)
Not a new result
- J. Kleinberg, “Navigation in a small world.” Nature 406 (2000) 845.
20,000 x 20,000 lattice
Observation
- Real networks often locally navigable
e.g., social network, world wide web Idea
- Distributed changes to topology
- Greedy changes to improve local navigability
- web surfers on network of home pages
- links changed based on speed of surfing
Origins of Navigability
- Same network as Kleinberg
- Dynamic, greedy rewiring process
- Global attractor for link-length distribution
- Gives routing time
Clauset/Moore Model (2003)
u v a b
P() → −αrewired αrewired ∼ d T ∼ [log(n)]2
- 1. choose random pair (for )
- 2. choose random tolerance on
- 3. if routing time , become frustrated
- 4. if frustrated, change random long-range link to have
length
Dynamics
[1, d(x, y)] (x, y) Tt Tt Tr ≥ Tt d = 1
Simulation Results
Initial Conditions All self-loops, i.e., Stop Criteria When link-length distribution stabilizes
P() ∼ −∞
Rewired Link-Lengths
1 4 19 84 369 1619 7098 31119 136423 598068 10 10
1
10
2
10
3
10
4
10
5
link−length (log−binned), l frequency f(l) = O(l−0.7789) n≈103 f(l) = O(l−0.8239) n≈104 f(l) = O(l−0.8454) n≈105 f(l) = O(l−0.8760) n≈106
Routing Times
- Measure mean routing times after stabilization
- Fast routing times
- Recall that (finite size effects)
T ∼ [log(n)]αTopt αTopt ∼ d
Routing Times
10
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10
2
10
3
10
4
10
5
10
6
10
7
25 50 75 100 125 150 network size, n mean routing time, T T(n) for α=d T(n) for αopt,αrewired T(n) =4.1log2(n)−7.39log(n) +6.08
How long until navigable?
- Let rewiring time be rewiring trials until
- grows as a low-order polynomial
τ(n) Trewired ≤ 1.01 · Topt τ(n) τ(n) ∼ n1.77
Time to Navigability
10
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10
2
10
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10
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10
5
10
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10
7
10 10
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10
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10
3
10
4
network size, n rewiring time, τ (trials per node) τ(n) data τ(n) =9+0.0304n0.77
Global Attractor
Initial Condition
- Link-length distribution
- Measure as function of
- Rewired distribution (eventually) independent of initial
condition αrewired α0
P() ∼ −α0
Independence
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 initial exponent, α0 rewired exponent, αrewired τ=75 τ=150 τ=300 τ=500
Analytics
- Distribution of tolerances
- Otherwise, we don’t know much
- If , then
- What is ?
Tt P(Tt) = log n − log Tt n − 1 − log n α = d E[new length] = E[old length] P(frustrated | d(u, v))
- Navigability can come from distributed behavior
- Natural/intuitive mechanism
- Process is adaptive to changes in size, etc.
- Analytics hard (full-history problems)
- Power laws emerge spontaneously - why?
- How does destination popularity effect rewired topol.
- Preprint at cond-mat/0309415