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Spectral Analysis and Spectral Analysis and Optimal Synchronizability Optimal Synchronizability
of Complex Networksof Complex Networks
复杂网络谱分析及最优同步性能研究复杂网络谱分析及最优同步性能研究
Spectral Analysis and Spectral Analysis and Optimal Synchronizability Optimal Synchronizability
of Complex Networksof Complex Networks
复杂网络谱分析及最优同步性能研究复杂网络谱分析及最优同步性能研究
Guanrong (Ron) Chen
City Univesity of Hong Kong
SynchronizationSynchronization
Synchrony can be essential 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
Spectra of network Laplacian matrices
Spectra and synchronizability of some typical complex networks
Networks with best synchronizability
ContentsContentsContentsContents
A linearly and diffusively coupled network:
f (.) – Lipschiz 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
jjijii xHacxfx
1
)()(
Laplacian matrix L = D – A where D = diag{ d1 , … , dN }
ni Rx Ni ,...,2,1
N 210For connected networks:
A General Dynamical Network ModelA General Dynamical Network Model
Linearized at equilibrium s :
N
jjijii xHacxfx
1
)()( Ni ,...,2,1ni Rx
Msf ||)(||
Only
is important
}{or ][ iij cac
f (.) Lipschitz, or assume:
i
N
jsxijsxii xDHacxDfx
ii
1
][)]([
Network SynchronizationNetwork SynchronizationNetwork SynchronizationNetwork Synchronization
Complete state
synchronization: Njitxtx jit
,,2,1,,0||)()(||lim 2
Synchronizing: if or if
N 210
320 10
Master stability equation: (L.M. Pecora and T. Carroll, 1998)
2 :,]][][[ cyDHDfy issi
maxL
Maximum Lyapunov exponent which is a function of
is called a synchronization regionmaxS
i
N
jsijsii xDHacxDfx
1
][)]([
Network Synchronization:Network Synchronization: CriteriaNetwork Synchronization:Network Synchronization: Criteria
A (conservative) sufficient condition:
0max L
Recall: Eigenvalues of
synchronizing if or if
Case III:Sync region
Case II:Sync region
Case I: No sync
N 210
),( 322 S),( 11 S
ADL
32
20
N
bigger is better bigger is better2N
2
Case IV: Union of intervals
210 c
Network Synchronization:Network Synchronization: CriteriaNetwork Synchronization:Network Synchronization: Criteria
Synchronizability characterized by Laplacian eigenvalues:
1. unbounded region (X.F. Wang and G.R. Chen, 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, G.R. Chen, and L. Huang, 2007 - 2009)
),(0, 11212 SN
),(0,/ 322212 SNN
),(),(),( 4321 mmS
A Brief HistoryA Brief History
Some theoretical results
Relation with network topology
Role in network synchronizability
Spectra of NetworksSpectra of NetworksSpectra of NetworksSpectra of Networks
Spectrum
- maximum, minimum, average degreeavgminmax , , ddd
maxmaxmin2 211
ddN
Nd
N
NN
Graph Theory Textbooks
F.M. Atay, T. Biyikoglu, J. Jost, Network synchronization: Spectral versus statistical properties, Physica D, 224 (2006) 35–41.
Theoretical Bounds of Laplacian EigenvaluesTheoretical Bounds of Laplacian EigenvaluesTheoretical Bounds of Laplacian EigenvaluesTheoretical Bounds of Laplacian Eigenvalues
NND N ...1
2
Nd
d
2
max
min
D – Diameter of the graph
)(avg Ad N
Theoretical Bounds of Laplacian EigenvaluesTheoretical Bounds of Laplacian EigenvaluesTheoretical Bounds of Laplacian EigenvaluesTheoretical Bounds of Laplacian Eigenvalues
TN ,...,, 21 (both in increasing order)
C. J. Zhan, G. Chen and L. F. Yeung, Physica A (2010)
Njj ,...,2,1|* idFor any node degree there exists a
such thatiiii dddd *
12
1
2
2
||||||||
||||
||||
||||
d
N
d
d
d
d
TNdddd ),...,,( 21
Distribution: (interlacing property)
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
12
2
12
2
2
2
||||/||||
||||
||||
||||
d
N
d
N
Nd
d
d
d
d
d
d
d
ii
i
i
i
TN ,...,, 21
12
1
2
2
||||||||
||||
||||
||||
d
N
d
d
d
d
T
Ndddd ),...,,( 21
ER random-graph networksN=2500, average of 2000 runs
BA scale-free networks
N=2500, average of 2000 runs
Difference between Eigenvalue and Degree SequencesDifference between Eigenvalue and Degree Sequences
C. J. Zhan, G. Chen and L. F. Yeung, Physica A (2010)
4210001000 d
Above: relative variancesbelow: relative errors
Upper Bounds for Largest EigenvaluesUpper Bounds for Largest Eigenvalues
}),(edges|max{ vudd vuN
}max{ uuN dd
ud
}nodes|||max{ GinvuNNdd vuvuN
vu NN
NN
- average degree of all neighbors of node u
- total number of common neighbors of u and v
W.N. Anderson, T.D. Morley, Eigenvalues of the Laplucian of a graph, Linear and Multilinear Algebra, 18 (1985) 141-145.R. Merris, A note on Laplacian graph eigenvalues, Linear Algebra and its Applications, 285 (1998) 33-35.O. Rojo, R. Sojo, H. Rojo, An always nontrivial upper bound for Laplacian graph eigenvalues, Linear Algebra and its Applications, 312 (2000) 155-159.
- for any graph
Lower Bounds for Smallest EigenvaluesLower Bounds for Smallest EigenvaluesLower Bounds for Smallest EigenvaluesLower Bounds for Smallest Eigenvalues
)2/(sin4))/cos(1(2 22 NeNe
max2 ))/cos(1)(/cos(2)]/2cos()/[cos(2 dNNNN
)/(12 NDve
e – edge connectivity
(number of edges whose removal will disconnect the graph
e.g., for a chain, e = 1 ; for a triangle, e = 2)
v – node connectivity
D – diameter
Regularly-connected networks Complete graph, ring, chain, star …
Random-graph networks For every pair of nodes, connect them with a certain probability. Poisson node distribution
Small-world networks Similar to random graph, but with short average distance and large clustering, with near-Poisson node distribution
Scale-free networks Heterogeneous, with power-law degree distribution
Spectra of Typical Complex NetworksSpectra of Typical Complex NetworksSpectra of Typical Complex NetworksSpectra of Typical Complex Networks
Piet Van Mieghem, Graph Spectra for Complex Networks (2011)
Complete graphs: 2K-rings:
K=1:
N - even:
K≠1:
Chains:
Stars:
1
2 ( 1)2 2 cos , 2,3,...,
K
il
i lK i N
N
Regularly-Connected NetworksRegularly-Connected Networks
1,..,.12
,4,12
,...,2,1,sin4,0 2
NNN
iN
i
1,..,2,1,2
sin4,0 2
NiN
i
1... ,0 12 N NN /1/2
NN ... ,0 2
NN
1/2 N
24sin , 1,2,..., 1i
i NN
Rectangular Random NetworksRectangular Random Networks
RRN: 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 ball of radius r > 0
Example: N =250
a = 1r = 0.15
E. Estrada and G. Chen (2015)
Na
ar
NN N
224
22
2log
1
)(8
)1(
1
E. Estrada and G. Chen (2015)
Theorem: For RRN, eigenvalues are bounded by
Lower bound
The worst case: all nodes are located on the diagonal
)1(
112
NNND
NN
and
Na
ar
NN N
224
22
2log
1
)(8
)1(
1
E. Estrada and G. Chen (2015)
Upper bound
Lemma 1: Diameter D = diagonal length / r
Lemma 2: Based on a result of Alon-Milman (1985)
ar
aD
14
ND
d 222
max2 log
8
Spectral Role in Network SynchronizationSpectral Role in Network SynchronizationSpectral Role in Network SynchronizationSpectral Role in Network Synchronization
Implication: small local changes affect eigenratio, hence network synchronizability, which however do not affect much the network
statistical properties in general: degree distribution, clustering,
average distance, …
• Recall: (bigger is better)
Topology affects Eigenvalues affects SyncTopology affects Eigenvalues affects Sync
Nd
d
2
max
min
2 2 S / S 0 < |S| /2N
S - any subgraph, with
Statistical properties depend on H, yet depends on S
2
Si SGj
ijaS
Statistics Determines Synchronizability?Statistics Determines Synchronizability?
Answer - Not necessarily
Example:
• They have same structural characteristics: Graphs and have same degree sequence {3,3,3,3,3,3}
• So, all nodes have degree 3 ; same average distance 7/5 ; and same node betweenness 2 ; same ……
Graph 1G Graph 2G
1G 2G
Z. S. Duan, G. R. Chen and L. Huang, Phys. Rev. E, 2007
But they have different synchronizabilities:
Eigenvalues of are and Eigenvalues of are and
Eigenratio satisfies:
1G
2G
2)(3)( 2212 GG
Nr /2 4.0)(5.0)( 21 GrGr
3,3,3,3,05,3,3,2,0
65
G1 G2
Answer: Not necessarily
• Lemma 1: For any given connected undirected graph G , all its nonzero eigenvalues will not decrease with the number of added edges; namely, by adding any edge e , one has
• Note:NiGeG ii ,..,2,1),()(
Z. S. Duan, G. R. Chen and L. Huang, Phys. Rev. E, 2007
Topology Determines Synchronizability ?Topology Determines Synchronizability ?
N2
N2
2
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 ??What Topology Good Synchronizability ??
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: Homogeneous + Symmetrical Topology
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
Objective: Find analytic solutions
02 Such that “row-sum = 0” and
matricesLaplacian ofset - * 2
*NNLMax
NLL
(Laplacian) (connected)
ProblemProblemProblemProblem
• Nishikawa et al., Phys. Rev. Lett. 91, 014101(2003), regular networks with uniform small (node or edge) betweenness [we found: edge betweenness is more important than node betweenness]
• Donetti et al., Phys. Rev. Lett. 95, 188701(2005) Entangled networks have biggest eigenratios [we found: not necessarily]
• Donetti et al., J. Stat. Mech.: Theory and Experiment 8, 1742(2006), algorithm based on algebraic graph theory; big spectral gap big eigenratio [we found: the opposite]
• Zhou et al., Eur. Phys. J. B. 60, 89(2007), algorithm based on smallest clustering coefficient
• Hui, Ann Oper. Res., July (2009), algorithm based on entropy • Xuan et al., Physica A 388, 1257(2009), algorithm based on short
average path length• Mishkovski et al., ISCAS, 681(2010), fast generating algorithm• Shi, Chen, Thong, Yan., IEEE CAS Magazine, 13, 1(2013), 3-
regular optimal graphs
ProgressProgressProgressProgress
• Homogeneity + Symmetry
• Same node degree
• Minimize average path length
• Minimize path-sum
• Maximize girth
1 Nd d
1 Ng g
1 Nl l
i ijj il l
Our ApproachOur ApproachOur ApproachOur Approach
D. Shi, G. Chen, W. W. K. Thong and X. Yan, IEEE Circ. Syst. Magazine, (2013) 13(1) 66-75
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 OptimizationNon-Convex OptimizationNon-Convex OptimizationNon-Convex Optimization
Optimal 3-Regular NetworksOptimal 3-Regular NetworksOptimal 3-Regular NetworksOptimal 3-Regular Networks
Optimal 3-Regular NetworksOptimal 3-Regular NetworksOptimal 3-Regular NetworksOptimal 3-Regular Networks
Optimal network topology (in the sense of having best possible synchronizability) should have
o Homogeneity
o Symmetry
o Shortest average path-length
o Shortest path-sum
o Longest girth
o Anything else ?
SummarySummarySummarySummary
Open ProblemOpen ProblemOpen ProblemOpen Problem
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 ?
N2
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 …… ???
Thank You !Thank You !Thank You !Thank You !Homogeneity Symmetry
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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[6] H.Hong, M.Y.Choi, B.J.Kim, Synchronization on small-world networks, PRE 69
(2004) 026139[7] F.M. Atay, T.Biyikoglu, J.Jost, Network synchronization: Spectral versus statistical
properties, Physica D 224 (2006) 35–41[8] A.Arenas, A.Diaz-Guilera, C.J.Perez-Vicente, Synchronization reveals topological
scales in complex networks, PRL 96(11) (2006 )114102 [9] A.Arenas, A.Diaz-Guilerab, C.J.Perez-Vicente, Synchronization processes in
complex networks, Physica D 224 (2006) 27–34[10] B.Mohar, Graph Laplacians, in: Topics in Algebraic Graph Theory, Cambridge
University Press (2004) 113–136[11] F.M.Atay, T.Biyikoglu, Graph operations and synchronization of complex
networks, PRE 72 (2005) 016217[12] T.Nishikawa, A.E.Motter, Maximum performance at minimum cost in network
synchronization, Physica D 224 (2006) 77–89
ReferencesReferencesReferencesReferences
[13] J.Gomez-Gardenes, Y.Moreno, A.Arenas, Paths to synchronization on complex networks, PRL 98 (2007) 034101
[14] C.J.Zhan, G.R.Chen, L.F.Yeung, On the distributions of Laplacian eigenvalues versus node degrees in complex networks, Physica A 389 (2010) 17791788
[15] T.G.Lewis, Network Science –Theory and Applications, Wiley 2009[16] T.M.Fiedler, Algebraic Connectivity of Graphs, Czech Math J 23 (1973) 298-305[17] W.N.Anderson, T.D.Morley, Eigenvalues of the Laplacian of a graph, Linear and
Multilinear Algebra 18 (1985) 141-145[18] R.Merris, A note on Laplacian graph eigenvalues, Linear Algebra and its
Applications 285 (1998) 33-35[19] O.Rojo, R.Sojo, H.Rojo, An always nontrivial upper bound for Laplacian graph
eigenvalues, Linear Algebra and its Applications 312 (2000) 155-159[20] G.Chen, Z.Duan, Network synchronizability analysis: A graph-theoretic
approach, CHAOS 18 (2008) 037102[21] T.Nishikawa, A.E.Motter, Network synchronization landscape reveals
compensatory structures, quantization, and the positive effects of negative interactions, PNAS 8 (2010)10342
[22] P.V.Mieghem, Graph Spectra for Complex Networks (2011) [23] D. Shi, G. Chen, W. W. K. Thong and X. Yan, Searching for optimal network
topology with best possible synchronizability, IEEE Circ. Syst. Magazine, (2013) 13(1) 66-75
AcknowledgementsAcknowledgements AcknowledgementsAcknowledgements
Prof Ernesto Estrada, University of Strathclyde, UK
Prof Xiaofan Wang, Shanghai Jiao Tong University
Prof Zhi-sheng Duan, Peking University
Prof Jun-an Lu, Wuhan University
Prof Dinghua Shi, Shanghai University
Dr Juan Chen, Wuhan University
Dr Choujun Zhan, City University of Hong Kong
Dr Wilson W K Thong, City University of Hong Kong
Dr Xiaoyong Yan, Beijing Normal University