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Random geometric graphs, Apollonian packings, number networks, and the Riemann hypothesis Dmitri Krioukov NU M. Kitsak (NU), R. Sinkovits (UCSD/SDSC) David Rideout & David Meyer UCSD/Math Marian Boguñá U. Barcelona Applied and Interdisciplinary Mathematics Seminar NU, Boston, February 2014

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Page 1: Random geometric graphs, Apollonian packings, number ... · Random geometric graphs, Apollonian packings, number networks, and the Riemann hypothesis Dmitri Krioukov NU M. Kitsak

Random geometric graphs,

Apollonian packings,

number networks, and

the Riemann hypothesis

Dmitri Krioukov NU

M. Kitsak (NU), R. Sinkovits (UCSD/SDSC)

David Rideout & David Meyer UCSD/Math

Marian Boguñá U. Barcelona

Applied and Interdisciplinary Mathematics Seminar

NU, Boston, February 2014

Page 2: Random geometric graphs, Apollonian packings, number ... · Random geometric graphs, Apollonian packings, number networks, and the Riemann hypothesis Dmitri Krioukov NU M. Kitsak

Random geometric graphs

• Discretizations of continuous spaces

• Riemannian

• Nodes are “atoms” of space

• Links reflect proximity

• Lorentzian

• Nodes are “atoms” of spacetime

• Links reflect causality

– Random geometric graphs in non-pathological Lorentzian

spacetimes define, up to the conformal factor, the spacetime

itself (Hawking/Malament theorem)

Page 3: Random geometric graphs, Apollonian packings, number ... · Random geometric graphs, Apollonian packings, number networks, and the Riemann hypothesis Dmitri Krioukov NU M. Kitsak

Random Riemannian graphs

• Given N nodes

• Hidden variables: – Node coordinates 𝜅 in a

Riemannian space

– 𝜌 𝜅 : uniform in the space

• Connection probability: 𝑝 𝜅 , 𝜅 ′ = Θ 𝑅 − 𝑑 𝜅 , 𝜅 ′

• Results: – Clustering:

Strong

– Degree distribution: Power law (hyperbolic)

𝜅 𝜅 ′ 𝑑 𝜅 , 𝜅 ′ < 𝑅

Page 4: Random geometric graphs, Apollonian packings, number ... · Random geometric graphs, Apollonian packings, number networks, and the Riemann hypothesis Dmitri Krioukov NU M. Kitsak

Random Riemannian graphs

• Given N nodes

• Hidden variables: – Node coordinates 𝜅 in a

Riemannian space

– 𝜌 𝜅 : uniform in the space

• Connection probability: 𝑝 𝜅 , 𝜅 ′ = Θ 𝑅 − 𝑑 𝜅 , 𝜅 ′

• Results: – Clustering:

Strong

– Degree distribution: Power law (de Sitter)

𝜅 𝜅 ′ 𝑑 𝜅 , 𝜅 ′ < 𝑅

Lorentzian

Lorentzian

0

0

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Taxonomy of Riemannian spaces

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Taxonomy of Lorentzian spaces

• De Sitter is dual to hyperbolic

• Random hyperbolic graphs N→∞

Random de Sitter graphs

Curvature 0 >0 <0

Space Minkowski De Sitter Anti-de Sitter

Curvature 0 >0 <0

Space Minkowski De Sitter Anti-de Sitter

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𝑑𝑠2 = 𝑑𝑥2 + 𝑑𝑦2 − 𝑑𝑧2 𝑟𝑃 = tanh𝑟𝐻2

𝑑𝑠2 = 𝑑𝑥2 + 𝑑𝑦2 + 𝑑𝑧2 𝑟𝐸 = −𝑖𝐸 𝑖𝑟𝐻, 2

𝑥2 + 𝑦2 − 𝑧2 = −1

𝑥2 + 𝑦2 < 1

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Degree distribution

P k ∼ k−γ γ = kB + 1

kB =2

−K

Clustering

𝑐 k ∼ k−1 maximized

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Growing hyperbolic model

• New nodes 𝑡 = 1,2, … are coming one at a

time

• Radial coordinate 𝑟𝑡 ∼ log 𝑡

• Angular coordinate 𝜃𝑡 ∈ 𝑈 0,2𝜋

• Connect to 𝑂 1 hyperbolically closest

existing nodes; or

• Connect to all existing nodes at distance

≲ 𝑟𝑡

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The key properties of the model

• Angular coordinates are projections of properly weighted combinations of all the (hidden) similarity attributes shaping network structure and dynamics

• Infer these (hidden) coordinates using maximum-likelihood methods

• The results describe remarkably well not only the structure of some real networks, but also their dynamics

• Validating not only consequences of this dynamics (network structure), but also the dynamics itself

• Prediction of missing links using this approach outperforms best-performing methods

Page 24: Random geometric graphs, Apollonian packings, number ... · Random geometric graphs, Apollonian packings, number networks, and the Riemann hypothesis Dmitri Krioukov NU M. Kitsak

The key properties of the model

• Angular coordinates are projections of properly weighted combinations of all the (hidden) similarity attributes shaping network structure and dynamics

• Infer these (hidden) coordinates using maximum-likelihood methods

• The results describe remarkably well not only the structure of some real networks, but also their dynamics

• Validating not only consequences of this dynamics (network structure), but also the dynamics itself

• Prediction of missing links using this approach outperforms best-performing methods

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𝑥2 + 𝑦2 − 𝑧2 = −1

𝑥2 + 𝑦2 − 𝑧2 = +1

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Light cones

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Random Lorentzian graphs

(causal sets)

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𝑥2 + 𝑦2 − 𝑧2 = −1

𝑥2 + 𝑦2 − 𝑧2 = +1

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Hyperbolic space

• −𝑧02 + 𝑧1

2 +⋯𝑧𝑑+12 = −𝑏2 =

1

𝐾

• 𝑧0 = 𝑏 cosh 𝜌, 𝑧𝑖 = 𝑏 𝜔𝑖 sinh 𝜌, 𝜌 =𝑟

𝑏

• 𝑑𝑠2 = 𝑏2 𝑑𝜌2 + sinh2 𝜌 𝑑Ω𝑑2

• 𝑑𝑉 = 𝑏𝑑+1 sinh𝑑 𝜌 𝑑𝜌 𝑑Φ𝑑 ≈

𝑏𝑏

2

𝑑𝑒𝑑𝜌 𝑑𝜌 𝑑Φ𝑑

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De Sitter spacetime

• −𝑧02 + 𝑧1

2 +⋯𝑧𝑑+12 = 𝑎2 =

1

𝐾

• 𝑧0 = 𝑎 sinh 𝜏, 𝑧𝑖 = 𝑎 𝜔𝑖 cosh 𝜏, 𝜏 =𝑡

𝑎

• 𝑑𝑠2 = 𝑎2 −𝑑𝜏2 + cosh2 𝜏 𝑑Ω𝑑2

• sec 𝜂 = cosh 𝜏, conformal time

• 𝑑𝑠2 = 𝑎2 sec2 𝜂 −𝑑𝜂2 + 𝑑Ω𝑑2

• 𝑑𝑉 = 𝑎𝑑+1 sec𝑑+1 𝜂 𝑑𝜂 𝑑Φ𝑑 =

𝑎𝑑+1 cosh𝑑 𝜏 𝑑𝜏 𝑑Φ𝑑 ≈ 𝑎𝑎

2

𝑑𝑒𝑑𝜏 𝑑𝜏 𝑑Φ𝑑

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Hyperbolic balls

• 𝜌0 =2

𝑑ln𝑁0

𝜈, current ball radius, i.e.,

the radial coordinate of new node 𝑁0

• Hyperbolic distance from the new node

𝑥 =𝑏 acosh cosh 𝜌 cosh 𝜌0 − sinh 𝜌 sinh 𝜌0 cos Δ𝜃 ≈

𝑏 𝜌 + 𝜌0 + 2 lnΔ𝜃

2

• 𝑥 < 𝑏𝜌0, connection condition

• 𝜌 + 2 lnΔ𝜃

2< 0, approximately

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Past light cones

• Δ𝜃 < Δ𝜂, connection condition

• Δ𝜃 < 𝜂0 − 𝜂 = asec cosh 𝜏0 − asec cosh 𝜏 ≈2 𝑒−𝜏 − 𝑒−𝜏0 , where 𝜂0, 𝜏0 is the current time

• Δ𝜃 < 2𝑒−𝜏, approximately

• 𝜌 + 2 lnΔ𝜃

2< 0, in the hyperbolic case

• The last two inequalities are the same iff

𝜏 =𝜌

2

• Mapping, two equivalent options:

– 𝑡 = 𝑟; 𝑎 = 2𝑏

– 𝑡 =𝑟

2; 𝑎 = 𝑏

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Node density

• Hyperbolic, non-uniform

– 𝑁 = 𝜈𝑒𝑑𝜌

2

– 𝑑𝑉 ≈ 𝑏𝑏

2

𝑑𝑒𝑑𝜌 𝑑𝜌 𝑑Φ𝑑

– 𝑑𝑁 = 𝛿 𝑒−𝑑𝜌

2 𝑑𝑉

• De Sitter, uniform

– 𝑑𝑉 ≈ 𝑎𝑎

2

𝑑𝑒𝑑𝜏 𝑑𝜏 𝑑Φ𝑑 = 𝑏

𝑑+1𝑒𝑑𝜌

2 𝑑𝜌 𝑑Φ𝑑

– 𝑑𝑁 = 𝛿 𝑑𝑉, where 𝛿 =𝑑𝜈

2𝑏𝑑+1𝜎𝑑, 𝜎𝑑 = ∫ 𝑑Φ𝑑 =

2𝜋𝑑+12

Γ𝑑+1

2

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Web of trust (PGP graph)

User A User B

Data A Email address,

PGP certificate

Trust

User A signs data B

Data B Email address,

PGP certificate

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Brain (fMRI graph)

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De Sitter universe

• The universe is accelerating

• Dark energy (73%) is an explanation

• Remove all matter from the universe

– Dark matter (23%)

– Observable matter (4%)

• Obtain de Sitter spacetime

– Solution to Einstein’s field equations for an

empty universe with positive vacuum energy

(cosmological constant Λ > 0)

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Exponential random graphs

• Intuition: – Maximum-entropy graphs under the constraints that expected values of some graph observables

are equal to given values

• Definition: – Set of graphs G with probability measure

𝑃 𝐺 = 𝑒− 𝐻(𝐺)𝑘𝐵𝑇

𝑍

where the partition function

𝑍 = 𝑒− 𝐻(𝐺)𝑘𝐵𝑇

𝐺

and

𝐻 𝐺 = 𝜔𝑖𝒪𝑖 𝐺

𝑖

is the graph Hamiltonian with Lagrange multipliers 𝜔𝑖 maximizing the Gibbs entropy

𝑆 = − 𝑃 𝐺 ln𝑃 𝐺

𝐺

under constraints

𝑃 𝐺 𝒪𝑖 𝐺 = 𝒪𝑖𝐺

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RHGs as ERGs

• Hamiltonian of graph G with adjacency matrix 𝐴𝑖𝑗 is

𝐻 𝐺 = 𝜔𝑖𝑗𝐴𝑖𝑗𝑖<𝑗

where 𝜔𝑖𝑗 = 𝑥𝑖𝑗 − 𝑅, and 𝑥𝑖𝑗 is the hyperbolic distance between nodes 𝑖 and 𝑗, and 𝑅 is the disk radius

• In the limit 𝑇 → ∞, 𝑘𝐵 → 0, 𝑘𝐵𝑇 = 𝛾 − 1 (soft configuration model or random graphs with a given expected degree sequence)

𝜔𝑖𝑗 → 𝜔𝑖 + 𝜔𝑗

𝐻 𝐺 → 𝜔𝑖𝑘𝑖𝑖

• 𝐻 = 𝑥𝑝 is the Berry-Keating Hamiltonian conjectured to be related, after quantization, to the Polya-Hilbert Hermitian operator (unknown) containing non-trivial Riemann zeros – Some extensions of 𝐻 = 𝑥𝑝 yield the Hamiltonian of non-interacting fermions

in de Sitter spacetime

– Graph edges in hyperbolic graphs are non-interacting fermions with energy 𝑥𝑖𝑗 (graph energy is 𝐻 𝐺 , while 𝑅 is the chemical potential of the fermionic gas)

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Euclidean Apollonian packing

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Spherical Apollonian packing

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Hyperbolic Apollonian packing

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Descartes theorem

• Descartes quadratic form 𝑄 𝑎, 𝑏, 𝑐, 𝑑 =2 𝑎2 + 𝑏2 + 𝑐2 + 𝑑2 − 𝑎 + 𝑏 + 𝑐 + 𝑑 2

• If 𝑎, 𝑏, 𝑐, 𝑑 are curvatures of any four mutually

tangent disks, then

– 𝑄 𝑎, 𝑏, 𝑐, 𝑑 = 0 in any Euclidean covering

– 𝑄 𝑎, 𝑏, 𝑐, 𝑑 = −4 in any spherical covering

– 𝑄 𝑎, 𝑏, 𝑐, 𝑑 = 4 in any hyperbolic covering

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Lorentz form

• There exists symmetric matrix 𝐽, 𝐽2 = 𝐼, such that

𝐿 =1

2𝐽𝑄𝐽𝑇 is

𝐿 𝑇, 𝑋, 𝑌, 𝑍 = −𝑇2 + 𝑋2 + 𝑌2 + 𝑍2 • That is, 𝐽 maps integers curvatures of tangent disk

quadruples to integer coordinates of points lying in 1+2-dimensional – light cone (Euclidean covering)

– hyperbolic space (spherical covering)

– de Sitter space (hyperbolic covering)

in the 1+3-dimensional Minkowski space

• The point density (CDF) is asymptotically uniform in all cases

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Apollonian questions

• Since the point density is uniform, hyperbolic

Apollonian covering networks are

asymptotically random de Sitter graphs with

power-law degree distributions, strong

clustering, etc.

• What are their exact properties?

• What are timelike-separated (connected)

versus spacelike-separated (disconnected)

pairs of tangent disk quadruples?

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Symmetries

• The group of isometries of – ℍ1,2 and d𝕊1,2 is the Lorentz group 𝑆𝑂(1,3) – Apollonian covering is its subgroup 𝑆𝑂(1,3, 𝑍)

• 𝑆𝑂 1,3 is isomorphic to the conformal group 𝑆𝐿(2, 𝐶) • 𝑆𝑂(1,3, 𝑍) is isomorphic to the modular group 𝑆𝐿(2, 𝑍) • 𝑆𝐿 2, 𝑋 (𝑋 = 𝐶, 𝑍) acts transitively on 𝑃𝑋2 by

linear fractional transformations 𝑎 𝑏𝑐 𝑑

: 𝑥

𝑦↦𝑎𝑥 + 𝑏𝑦

𝑐𝑥 + 𝑑𝑦

– In particular, 𝑆𝐿 2, 𝑍 acts transitively on (ir)reducible fractions

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Uniformity of Farey numbers

• The distribution of Farey rationals on [0,1] is very close to uniform

• Let 𝑓𝑘 , 𝑘 = 1,… ,𝑁, be a sequnce of Farey numbers 𝑝𝑘

𝑞𝑘

for some 𝑄 ≥ 𝑞𝑘 (𝑁 𝑄 ∼ 3𝑄2/𝜋2 is given by Euler’s totient function)

• Let 𝑔𝑘 =k

N, and 𝑑𝑘 = 𝑓𝑘 − 𝑔𝑘

• Franel-Landau theorem: the Riemann hypothesis is equivalent to

𝑑𝑘 = 𝒪 𝑄12+𝜖

𝑘

for any 𝜖 > 0

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Divisibility networks

• Two natural numbers are connected if one

divides the other or if they are not coprime

• Degree distribution is a power law with

exponent 𝛾 = 2

• Clustering is strong 𝑐 =1

2

• That is, the same as in random de Sitter graphs

or hyperbolic Apollonian packings

• Many other important statistical structural

properties also appear similar in experiments

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Number-geometric questions

• Are their any (soft) embeddings of Farey or

divisibility networks into (de Sitter) spaces?

• If such embeddings are found, then timelike

distances will have clear interpretations

• But what are spacelike distances??? What is

the hidden geometry of numbers???

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• F. Papadopoulos, M. Kitsak, M. Á. Serrano, M. Boguñá, and D. Krioukov, Popularity versus Similarity in Growing Networks, Nature, v.489, p.537, 2012

• D. Krioukov, M. Kitsak, R. Sinkovits, D. Rideout, D. Meyer, and M. Boguñá, Network Cosmology, Nature Scientific Reports, v.2, 793, 2012 Press: Space, Time, … UCSD, SDSC, Space, Time, TheRegister, CBS, HuffingtonPost, HuffingtonPostUK, PopularScience, LiveScience, Slashdot, Robots.Net, PhysOrg, ScienceDaily, TGDaily, DigitalJournal, Vesti, MK, LifeNews, theRunet