sync - department of eegchen/pdf/sync.pdf · • lemma: for any given connected undirected graph g,...
TRANSCRIPT
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SYNC
GRChen / EE / CityU
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Sync
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Synchrony can be essential
IEEE Signal Processing Magazine (2012)
“Clock synchronization
is a critical component in
the operation of wireless
sensor networks, as it
provides a common time
frame to different nodes.”
2009
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Network synchronization and criteria
Network spectra and synchronizability
Networks with best synchronizability
Networks with good controllability and strong robustness against attacks
Contents
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An undirected network:
f (.) – Lipschitz Coupling strength c > 0
If there is a connection between node i and node j (j ≠ i), then aij = aji = 1 ; otherwise, aij = aji = 0 and aii = 0, i = 1, … , N
A = [aij] – Adjacency matrix H – Coupling matrix function
N
j
jijii xHacxfx1
)()(
Laplacian L = D – A , D = diag{ d1 , … , dN } (node degrees)
n
i Rx Ni ,...,2,1
N 210For connected networks, eigenvalues:
A General Dynamical Network Model
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(Complete state)
Synchronization:
Numerical example:
Njitxtx jit
,,2,1,,0| |)()(| |lim 2
N
j
jijii xHacxfx1
)()( Ni ,...,2,1n
i Rx
Network Synchronization
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Put all equations together with
Then linearize it at equilibrium s :
N
j
jijii xHacxfx1
)()( Ni ,...,2,1n
i Rx
Msf ||)(||
After linearization, perform local analysis
Only
is important
}{or iccA f (.) Lipschitz, or assume:
Network Synchronization: Analysis
x)]]([[)]]([[x sHAcsfIN
TT
N
TT xxx ],...,,[x 21
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Synchronizing: if or if
N 210
320 10
Master stability equation: (L.M. Pecora and T. Carroll, 1998)
},...,3,2),( inf{,y)]]([)]([[y NiscsHsf i
maxL
Maximum Lyapunov exponent is a function of maxL
-- sync region maxS
Network Synchronization: Criteria
),( 11 S ),( 322 S
maxL maxL
x)]]([[)]]([[x sHAcsfIN
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Recall: Laplacian eigenvalues:
synchronizing if or if
Case III:
Sync region Case II:
Sync region
Case I:
No sync
N 210
),( 322 S),( 11 S
32
20
N
bigger is better bigger is better 2N
2
Case IV:
Union of
intervals
210 c
Network Synchronization: Criteria
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Synchronizability characterized by Laplacian eigenvalues:
1. unbounded region (X.F. Wang and GRC, 2002)
2. bounded region (M. Banahona and L.M. Pecora, 2002)
3. union of several disconnected regions
(A. Stefanski, P. Perlikowski, and T. Kapitaniak, 2007)
(Z.S. Duan, C. Liu, GRC, and L. Huang, 2007 - 2009)
),(0, 11212 SN
),(0,/ 322212 SNN
),(),(),( 4321 mmS
In retrospect
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Some theoretical results
Relation with network topology
Role in network synchronizability
Spectra of Networks
Spectrum
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Graph Theory Textbooks
For example:
P. V. Mieghem, Graph Spectra for Complex Networks (2011)
Theoretical Bounds of Laplacian Eigenvalues
There are many classical results in graph special analysis
bigger is better bigger is better 2N
2
Concern: upper and lower bounds of Laplacian eigenvalues
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Theoretical Bounds of Laplacian Eigenvalues
TN ,...,, 21
(both in increasing order)
C. Zhan, GRC and L. Yeung (2010)
Njj ,...,2,1|* idFor any node-degree there exists a
such that
Nidddd iiii ,...,2,1 ,*
12
1
2
2
||||||||
||||
||||
||||
d
N
d
d
d
d
T
Ndddd ),...,,( 21
Distribution:
Node-degree sequence eigenvalue sequence
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Lemma (Hoffman-Wielanelt, 1953)
For matrices B – C = A: 2
1
2 |||| |)()(| F
n
i ii ACB
Frobenius Norm
For Laplacian L = D – A: 2
1
2 |||| |)(| F
n
i ii AdL
n
i i
n
i
n
j ijF daA11 1
22 ||||||
Cauchy Inequality
1
2
2
1
2
2
2
2
||||/||||
||||
||||
||||
d
N
Nd
d
d
d
d
d
d
d
i
i
i
i
TN ,...,, 21
12
1
2
2
||||||||
||||
||||
||||
d
N
d
d
d
d
T
Ndddd ),...,,( 21
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Random Graphs
Example
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Rectangular Random Graphs
N nodes are randomly uniformly and independently
distributed in a unit rectangle 1 with ],[ 22 baRba
(It can be generalized to higher-dimensional setting)
Two nodes are connected by an edge if they are inside
a disc of radius r > 0
Example:
N =200
a = 40
r = 2.5
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Na
ar
NN N
2
24
2
2
2log
1
)(8
)1(
1
E. Estrada and GRC (2015)
Theorem: Eigenvalue ratio is bounded by
Lower bound:
The worst case: all nodes are located on the diagonal
diameter)( )1(
112
D
NNND NN and
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Na
ar
NN N
2
24
2
2
2log
1
)(8
)1(
1
E. Estrada and GRC (2015)
Upper bound:
Lemma 1: Diameter D = diagonal length / r
Lemma 2: Based on a result of Alon-Milman (1985)
ar
aD
14
ND
k 2
22
max2 log
8
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Network Topology and Synchronizability
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Topology Determines Synchronizability?
Answer: Yes or No
This makes the situation complicated and the study difficult
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Answer: Yes or No
• Lemma: For any given connected undirected graph G ,
by adding any new edge e , one has
• Note:
NiGeG ii ,..,2,1),()(
Z.S. Duan, GRC and L. Huang (2007)
More Edges Better Synchronizability ?
N
2
N
2
2
This also makes the situation complicated and the study difficult
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Example:
Given Laplacian
Q: How to replace 0 and -1 (while keeping the connectivity
and all row-sums = 0), such that = maximum ?
2 0 0 0 1 1
0 2 1 1 0 0
0 1 3 0 1 1
0 1 0 3 1 1
1 0 1 1 4 1
1 0 1 1 1 4
L
2 N
What Topology Good Synchronizability ?
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Answer:
*
3 1 0 1 0 1
1 3 1 0 1 0
0 1 3 1 0 1
1 0 1 3 1 0
0 1 0 1 3 1
1 0 1 0 1 3
L
2 N = maximum
Observation:
Homogeneity + Symmetry
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With the same numbers of node and edges, while
keeping the connectivity, what kind of network has
the best possible synchronizability?
Computationally, this is NP-hard:
2
* * 0, 0 0, 0
[ ] [ ]max max min max
T T
T T
T TA A A A x e x x e x
N
x D A x x D A x
x x x x
1
N
iid Nk
02 Such that and
matricesadjacency ofset - * 2
*NNAMax
NAA
(total degree = constant) (connected)
Problem
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• Homogeneity + Symmetry
• Same node degree
• Shortest average path length
• Shortest path-sum
• Longest girth
1 Nd d
1 Ng g
1 Nl l
i ijj il l
Our Approach
D.H. Shi, GRC, W.W.K. Thong and X. Yan (2013)
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Illustration:
Grey:networks with same numbers of nodes and edges
Green:degree-homogeneous networks
Blue:networks with maximum girths
Pink:possible optimal networks
Red:near homogenous networks
White:Optimal
solution location
Non-Convex Optimization
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Optimal 3-Regular Networks
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Optimal 3-Regular Networks
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Open Problems
Looking for optimal solutions:
Nd
d
d
001
0
0
01
101
2
1
Nd
d
d
001
0
0
01
101
2
1
Given: Move which -1 can maximize ? N
2
Where to add -1 can maximize ? N
2
Nd
d
d
001
01
0
011
101
2
1
Where to delete -1 …………………… can maximize ? N
2
And so on …… ???
Constraints:
keeping the graph
connectivity and
all row-sums = 0
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Good Sync-Controllability
Strong Robustness against Attacks
Multiplex Congruence Networks
of Natural Numbers
Number Theory and Complex Networks
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三三数之剩二,五五数之剩三,七七数之剩二。
N = 357 = 105
(270+321+215)/105 with remainder 23 (Answer: x = 23)
2(mod3)
3(mod5)
2(mod 7)
x
x
x
(Congruence Equations)
Chinese Remainder Theorem
《孙子歌诀》 三人同行七十稀,五树梅花廿一枝,七子团圆正半月,除百零五便得知。
Notation: Let n (> 1), x (> n), a ( n) be integers
If n is divisible by (x - a),then x and a are congruent modulo n ,denoted as x ≡ a (mod n)
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Let n1, ..., nk (all >1) be integers
If ni are pairwise coprime, then for integers a1, ..., ak ,
there exist infinitely many integers x satisfying
(congruence)
And, any two such x are congruent modulo N = n1 ... nk
(Only natural numbers a1, ..., ak are discussed here)
) (mod
) (mod 11
kk nax
nax
Chinese Remainder Theorem
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Given a natural number r ,there exist infinitely many pairs
of natural numbers (a,m) satisfying a ≡ r (mod m)
Example:
For r = 2, one has (a,m) = (3,5), (5,7), (7,11), …
namely, 3 ≡ 2 (mod 5), 5 ≡ 2 (mod 7), 7 ≡ 2 (mod 11), …
Connecting 3 5, 5 7, 7 11, …, … N
or 3 5, 5 7, 7 11, …, … N
yields a directed congruence network of N nodes
Different r yields different Multiplex Congruence Network (MCN),
denoted as G(r, N)
X.Y. Yan, W.X. Wang, GRC and D.H. Shi (2015)
Congruence Networks
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(a) MCN: G (r = {1,2,3},9) (b) Degree distribution, N = 10000
MCNs are scale-free networks with node-degree distribution 2~)( kkP
Example
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(a) MCNs have chain-structures (b) Number of control nodes needed: nD
Robustness against attacks: CN = Congruence Network (N = 100); SF = Scale-free Network (N = 100)
TA = Targeted Attack; RA = Random Attack
Sync-Controllability and Robustness
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Controllability: is Controller (Driver Node)
Chain structure is good for both
controllability and robustness against attacks
Graphical Explanation
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Thank You !
Homogeneity Symmetry
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[1] F.Chung, Spectral Graph Theory, AMS (1992, 1997)
[2] T.Nishikawa, A.E.Motter, Y.-C.Lai, F.C.Hoppensteadt, Heterogeneity in oscillator networks: Are smaller worlds easier to synchronize? PRL 91 (2003) 014101
[3] H.Hong, B.J.Kim, M.Y.Choi, H.Park, Factors that predict better synchronizability on complex networks, PRE 65 (2002) 067105
[4] M.diBernardo, F.Garofalo, F.Sorrentino, Effects of degree correlation on the synchronization of networks of oscillators, IJBC 17 (2007) 3499-3506
[5] M.Barahona, L.M.Pecora, Synchronization in small-world systems, PRL 89 (5) (2002)054101
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Acknowledgements
Prof Xiaofan Wang, Shanghai Jiao Tong University
Prof Dinghua Shi, Shanghai University
Prof Zhi-sheng Duan, Peking University
Prof Ernesto Erstrada, University of Strathclyde, UK
Dr Choujun Zhan, City University of Hong Kong
Dr Wilson W K Thong, City University of Hong Kong
Dr Xiaoyong Yan, Beijing Normal University
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