Statistical analysis of weighted networks
Microsoft Word - Statistical analysis of weighted networks
Statistical Analysis of Weighted Networks 
Antoniou Ioannis, Tsompa Eleni  
Mathematics Department, Aristotle University, Thessaloniki, 54124, Greece 
iantonio@math.auth.gr , etsompa@math.auth.gr  
Abstract 
The purpose of this paper is to assess the statistical characterization of weighted 
networks in terms of the generalization of the relevant parameters, namely average 
path length, degree distribution and clustering coefficient. Although the degree 
distribution and the average path length admit straightforward generalizations, for the 
clustering coefficient several different definitions have been proposed in the 
literature. We examined the different definitions and identified the similarities and 
differences between them. In order to elucidate the significance of different 
definitions of the weighted clustering coefficient, we studied their dependence on the 
weights of the connections. For this purpose, we introduce the relative perturbation 
norm of the weights as an index to assess the weight distribution. This study 
revealed new interesting statistical regularities in terms of the relative perturbation 
norm useful for the statistical characterization of weighted graphs.     
PACS: 89.75.Hc; 02.10.Ox; 05.10.-a; 87.23.Kg 
Keywords 
Weighted networks, weighted graphs, weighted clustering coefficient, weighted 
characteristic path length 
1. Introduction 
Complex systems may also [1] emerge from a large number of interdependent and 
interacting elements. Networks have proven to be effective models of natural or man-
made complex systems, where the elements are represented by the nodes and their 
interactions by the links. Typical well known examples include communication and 
transportation networks, social networks, biological networks [2, 3, 4, 5].  
Although the statistical analysis of the underlying topological structure has been very 
fruitful [2, 3, 4, 5] it was limited due to the fact that in real networks the links may 
have different capacities or intensities or flows of information or strengths. For 
example, weighted links can be used for the Internet, to represent the amount of data 
exchanged between two hosts in the network. For scientific collaboration networks 
the weight depends on the number of coauthored papers between two authors. For 
airport networks, it’s either the number of available seats on direct flight connections 
between airports i and j or the actual number of passengers that travel from airport i 
to j. For neural networks the weight is the number of junctions between neurons and 
for transportation networks it’s the Euclidean distance between two destinations.  
The diversification of the links is described in terms of weights on the links. Therefore, 
the statistical analysis has to be extended from graphs to weighted complex 
networks. If all links are of equal weight, the statistical parameters used for 
unweighted graphs are sufficient for the statistical characterization of the network. 
Therefore, the statistical parameters of the weighted graphs should reduce to the 
corresponding parameters of the conventional graphs if all weights are put equal to 
unity.  
Complex graphs are characterized by three main statistical parameters, namely the 
degree distribution, the average path length and the clustering coefficient. We shall 
briefly mention the definitions for clarity and for a better understanding of the 
proposed extensions of these parameters for weighted graphs. 
The structure of a network with N nodes is represented by a NxN binary matrix 
ijA {a }= , known as adjacency matrix, whose element ija  equals 1, when there is a 
link joining node i to node j and 0 otherwise (i, j=1,2,…,N). 
In the case of undirected networks with no loops, the adjacency matrix is symmetric 
( ij jia a= ) and all elements of the main diagonal equal 0 ( iia 0= ). 
The degree ik  of a node i is defined as the number of its neighbours, i.e. the number 
of links incident to node i: 
j (i)
= ∑      (1) 
where ija  the elements of the adjacency matrix A and (i)Π  the neighborhood of 
node i. 
The degree distribution is the probability that some node has k connections to other 
nodes and it is usually described by a power law P(k) ~ k−γ , with 2 3≤ γ ≤ . 
The characteristic path length of a network is defined as the average of the 
shortest path lengths between any two nodes: 
N(N 1)
− ∑     (2) 
where ijd  is the shortest path length between i and j, defined as the minimum 
number of links traversed to get from node i to node j. 
In many real networks it is found that the existence of a link between nodes i and j 
and between nodes i and k enhances the probability that node j will also be 
connected to node k. This tendency of the neighbours of any node i to connect to 
each other, is called clustering and is quantified by the clustering coefficient iC , 
which is the fraction of triangles in which node i participates, to the maximum 
possible number of such triangles: 
( ) ( )
ij jk ki
i i i i
a a a
k k 1 k k 1
, ik 0,1≠     (3) 
where i ij jk ki
n a a a
= ∑  is the actual number of triangles in which node i 
participates i.e. the actual number of links between the neighbours of node i, and 
( )i ik k 1 / 2−  is the maximum possible number of links, when the subgraph of 
neighbours of node i is completely connected. 
The clustering coefficient iC  equals 1, if node i is the center of a fully interconnected 
cluster and equals 0, if the neighbours of node i are not connected to each other. 
Ιn order to characterize the network as a whole, we usually consider the average 
clustering coefficient C over all the nodes. We may also consider the average 
clustering coefficient C(k) over the node degree k. 
Studies of real complex networks have shown that their connection topology is 
neither completely random nor completely regular, but lies between these extreme 
cases. Many real networks share features of both extreme cases. For example, the 
short average path length, typical of random networks, comes along with large 
clustering coefficient, typical of regular lattices. The coexistence of these attributes 
defines a distinct class of networks, interpolating between regular lattices and 
random networks, known today as small world networks [3, 4, 5, 6]. Another class of 
networks emerges when the degree distribution is a power law (scale free) 
distribution, which signifies the presence of a non negligible number of highly 
connected nodes, known as hubs. These nodes, with very large degree k compared 
to the average degree <k>, are critical for the network’s robustness and vulnerability. 
These networks are known today as scale free networks [2, 3, 4, 7]. 
The purpose of this paper is to assess the statistical characterization of weighted 
networks in terms of proper generalizations of the relevant parameters, namely 
average path length, degree distribution and clustering coefficient. After reviewing 
the definitions of the weighted average path length, weighted degree distribution and 
weighted clustering coefficient in section 2, we compare them in section 3. Although 
the degree distribution and the average path length admit straightforward 
generalizations, for the clustering coefficient several different definitions have been 
proposed. In order to elucidate the significance of different definitions of the weighted 
clustering coefficient, we studied their dependence on the weights of the connections 
in section 4, where we introduce the relative perturbation norm as an index to assess 
the weight distribution. This study revealed new interesting statistical regularities in 
terms of the relative perturbation norm useful for the statistical characterization of 
weighted graphs. 
2. Statistical parameters of weighted networks 
The weights of the links between nodes are described by a NxN matrix ijW {w }= .  
The weight ijw  is 0 if the nodes i and j are not linked. We will consider the case of 
symmetric positive weights ( ij jiw w 0= ≥ ), with no loops ( iiw 0= ). 
In order to compare different networks or different kinds of weights, we usually 
normalize the weights in the interval [0,1], by dividing all weights by the maximum 
weight. Τhe normalized weights are ij
max(w )
The statistical parameters for weighted networks are defined as follows. 
The node degree i ij
j (i)
= ∑ , which is the number of links attached to node i, is 
extended directly to the strength or weighted degree, which is the sum of the 
weights of all links attached to node i: 
j (i)
= ∑      (4) 
The strength of a node takes into account both the connectivity as well as the 
weights of the links. 
The degree distribution is also extended for the weighted networks to the strength 
distribution P(s), which is the probability that some node’s strength equals s. 
Recent studies indicate power law aP(s) ~ s−  [8, 9, 10]. 
There are two different generalizations of the characteristic path length in the 
literature, applicable to transportation and communication networks. In the case of 
transportation networks the weighted shortest path length ijd  between i and j, is 
defined as the smallest sum of the weights of the links throughout all possible paths 
from node i to node j [11, 12]: 
ij ij
d min w= ∑      (5) 
The weight describes physical distances and/or cost usually involved in 
transportation networks. The capacity/intensity/strength/efficiency of the connection 
is inversely proportional to the weight.  
However, this definition is not suitable for communication networks, where the 
efficiency of the communication channel between two nodes is proportional to the 
weight. The shortest path length in case of communication networks is defined as: 
i, j ij
d min
= ∑      (6) 
To our knowledge, the latter definition has been used by Latora and Marchiori [13, 
14] for the definition of the “efficiency” of the network, as inversely proportional to the 
shortest path length ijd . 
The weighted characteristic path length for both cases is the average of all shortest 
path lengths and it is calculated by formula (2). 
We found in the literature six proposals for the definitions of the weighted clustering 
coefficient, which we shall review. 
 Zhang et. al. (2005) [15] definition: 
ij jk ki
w,i 2
ij ij
w w w
    (7) 
The weights in this definition are normalized. The idea of the generalization is the 
substitution of the elements of the adjacency matrix by the weights in the nominator 
of formula (3), as for the denominator the upper limit of the nominator is obtained in 
order to normalize the coefficient between 0 and 1. The definition originated from 
gene co-expression networks.  
As shown by Kalna et. al. (2006) [16] an alternative formula that may apply for this 
definition is  
ij jk ki
ij ik
j k j
w w w
 Lopez-Fernandez et. al. (2004) [17] definition: 
j,k i i i
k (k 1)∈Π
∑     (8) 
The weights in this definition are not normalized. The idea of the generalization is the 
substitution of the number of links that exist between the neighbours of node i in 
formula (3) by the weight of the link between the neighbours j and k. The definition 
originated from an affiliation network for committers (or modules) of free, open 
source software projects. 
 Onnela et. al. (2005) [18] definition:  
ij jk ki
w w w
k (k 1)
    (9) 
The weights in this definition are normalized. The quantity ( )
ij jk kiI(g) w w w=  is 
called “intensity” of the triangle ijk. The concept for this generalization is to substitute 
the total number of the triangles in which node i participates, by the intensity of the 
triangle, which is geometric mean of the links’ weights.  
 Barrat et. al. (2004) [8] definition: 
ij ikB
w,i ij jk ki
j,ki i
C a a a
s (k 1) 2
− ∑     (10) 
The weights in this definition are not normalized. The idea of the generalization is the 
substitution of the elements of the adjacency matrix in formula (3), by the average of 
the weights of the links between node i and its neighbours j and k with respect to 
normalization factor i is (k 1)−  which ensures that 
w,i0 C 1≤ ≤ . This definition was 
used for airport and scientific collaboration networks. 
 Serrano et. al. (2006) [19] definition 
ij ik kj
w,i 2
w w a
s (1 Y )
    (11) 
where 
= ⎜ ⎟
∑  has been named “disparity”. 
The weights in this definition are not normalized. This formula is used for the 
generalization of the average clustering coefficient with degree k, which has a 
probabilistic interpretation just as the unweighted clustering coefficient. 
 Holme et. al. [20] definition:  
ij jk ki
ij ij ik
j k j
w w w
max(w ) w w
     (12) 
The only difference between formulas (7) and (12) is that (12) is divided by ijmax(w ) . 
We shall not discuss this definition in the comparison because the essence of the 
comparison is already addressed by definition (7). 
 Li et. al. (2005) [21] definition of the weighted clustering coefficient, is another 
version of the Lopez-Fernandez proposal (8). 
3. The relation between the different weighted clustering coefficients 
1. All definitions reduce to the clustering coefficient (3), when the weights ijw  are 
replaced by the adjacency matrix elements.  
2. All weighted clustering coefficients reduce to 0 when there are no links between 
the neighbours of node i, that is when jk jka w 0= = . 
3. In the other extreme, all weighted clustering coefficient take the value 1 when all 
neighbours of node i are connected to each other. Formulas (7) and (8) take the 
value 1 if the weights between the neighbours of the node i are 1, independently 
of the weights of the other links. Formula (9) takes the value 1, if and only if all 
the weights are equal to 1. Formulas (10) and (11) take the value 1 for all fully 
connected graphs, independently of all the weights.  
These calculations are presented in Appendix A. 
4. We calculated the values of the weighted clustering coefficients of node i 
participating in a fully connected triangle. Formulas (7) and (8) take the value 
jkw  of the weight of the link between neighbours j and k, of node i. Formula (9) 
becomes equal to the intensity of the triangle O 1/3w,i ij jk kiC (w w w )=  for all nodes of 
the triangle. Formulas (10) and (11) take the value 1 for all fully connected 
graphs, independently of all weights.  
These calculations are presented in Appendix B. 
4. The dependence of the weighted clustering coefficients on the weights 
In order to understand the meaning of the different proposals-definitions (7), (8), (9) 
(10) and (11) of the weighted clustering coefficient we shall examine their 
dependence on the weights, without alteration of the topology of the graph. We 
simply examine the values of these definitions for different distributions of weights, 
substituting the nonzero elements of the adjacency matrix A by weights normalized 
between 0 and 1. 
A way to distinguish and compare different weight distributions over the same graph, 
is in terms of the relative perturbation norm 
, which gives the percentage of 
the perturbation of the adjacency matrix introduced by the weights. For simplicity, we 
considered the L2 norm. 
We shall examine now the dependence of the weighted clustering coefficient with 
respect to the relative perturbation norms for several different weight distributions as 
well as for different graphs. We have examined many networks from 20 up to 300 
nodes with different topologies that were generated by the networks software PAJEK 
[22]. The weights examined are randomly generated numbers following uniform or 
normal distributions with several parameter values, so that the percentages of the 
perturbations scale from 0-90% increasing by 10% at each perturbation. All 
simulations gave rise to the same results, figs. 3 and 4, representing the typical 
trends of random and scale free networks, figs.1 and 2. It is remarkable to 
emphasize again that in all cases the same trends appear demonstrating a clear 
dependence on the relative perturbation norm only and no dependence on the values 
of weights on specific links.  
Figure 1. The random network (Erdos-Renyi model) examined consists of 100 nodes and was 
generated by the networks software PAJEK [22]. The clustering coefficient for the unweighted 
network is 0.3615. 
Figure 2. The scale-free network (Barabasi-Albert extended model) examined consists of 100 
nodes and was generated by the networks software PAJEK [22]. The clustering coefficient for 
the unweighted network is 0.6561. 
Figure 3. The values of all five weighted clustering coefficients Zhang et. al. Zw,iC ( ) , Lopez-
Fernandez et. al. Lw,iC ( ) , Onnela et. al. 
w,iC ( ) , Barrat et. al. 
w,iC ( )  and  Serrano et.al. 
w,iC ( ) , in terms of the relative perturbation norm for the random network (Erdos-Renyi 
model) with 100 nodes.  
(A). The weights are randomly generated numbers following the uniform distribution.  
(B). The weights are randomly generated numbers following the normal distribution. 
Figure 4. The values of all five weighted clustering coefficients Zhang et. al. Zw,iC ( ) , Lopez-
Fernandez et. al. Lw,iC ( ) , Onnela et. al. 
w,iC ( ) , Barrat et. al. 
w,iC ( )  and  Serrano et.al. 
w,iC ( ) , in terms of the relative perturbation norm for the scale free network (Barabasi-Albert 
extended model) with 100 nodes.  
(A). The weights are randomly generated numbers following the uniform distribution.  
(B). The weights are randomly generated numbers following the normal distribution. 
We observe in all cases a clear trend dependence of the values of all five weighted 
clustering coefficients, in terms of the relative perturbation norm of the weighted 
network. This demonstrates clearly first of all that the relative perturbation norm is a 
reliable index of the weight distribution. The Zhang et. al. (7), Lopez-Fernandez et. al. 
(8) and Onnela et. al. (9), weighted clustering coefficients follow the same trend, 
decaying smoothly as the relative perturbation norm increases. More specifically the 
trends of Zhang et. al. (7) and Lopez-Fernandez et. al. (8) almost coincide, while the 
trend of Onnela et. al. (10) varies slightly from the other two.  
The weighted clustering coefficients of Barrat et. al. (10) and Serrano et.al. (11) do 
not change (variations appear after the first two decimal digits), regardless of the size 
of the network or the distribution of the weights. As mentioned in section 3, these 
coefficients are independent of the weights when the graph is completely connected. 
We notice here however, that weighted clustering coefficients (10) and (11) are 
independent of the weights for any graph.  
5. Concluding remarks  
1. The clear trend dependence of the values of all five weighted clustering 
coefficients in terms of the relative perturbation norm shows that the proposed 
relative perturbation norm is a reliable index of the weight distribution. The 
meaning of the decaying trend of weighted clustering coefficients Zhang et. al. (7), 
Lopez-Fernandez et. al. (8) and Onnela et. al. (9), with respect to the increase of 
the relative perturbation norm is quite natural. The clustering decreases almost 
linearly as the weights “decrease”.  
2. We presented in Appendices A and B the calculations demonstrating that all 
definitions reduce to the clustering coefficient (3), when the weights ijw  are 
replaced by the adjacency matrix elements. The values of the weighted clustering 
coefficients of node i participating in a fully connected triangle are presented for 
completeness because we did not found them in the literature. 
3. The results presented in figures 3 and 4 were necessary to obtain in order to have 
a minimal understanding of the statistical analysis of weighted networks, in order 
to proceed to applications on real networks.  
Acknowledgements 
We would like to thank Prof. Kandylis D. from the Medical School of Aristotle 
University of Thessaloniki who showed to us the significance of weighted networks in 
cognitive processes. We also thank Drs. Serrano M. A., Boguñá M. and Pastor-
Satorras R. who pointed out their work to us. 
APPENDIX A. Calculations on the weighted clustering coefficient 
The definitions (7)-(11) reduce to the clustering coefficient (3), when the weights ijw  
are replaced by the adjacency matrix elements. 
1. Zhang et. al. (2005) 
ij jk ki
w,i 2
ij ij
w w w
The proof is presented by the authors. 
For example, for a fully connected network with four nodes  
4 4 4
21 31 411j jk k1 1j j2 j3 j4
j 1 k 1 j 1
2 2 2 2 24 4
12 13 14 12 13 142
1j 1j
j 1 j 1
12 23 31 12 24 41 13 32 21 13 34 41 14 42 21 14 43 31
12 13 13 14 12 14
w w w w w w w w w w
w w w w w w
w w w w w w w w w w w w w w w w w w
      
w w w w w w
≠ ≠ ≠
⎜ ⎟⎜ ⎟
= = =
+ + − − −
12 23 31 13 34 41 12 24 41
12 13 13 14 12 14
w w w w w w w w w
w w w w w w
w,1C 1=  when 23 34 24w w w 1= = =  
2. Lopez-Fernandez et. al. (2004) 
j,k i i i
k (k 1)∈Π
this formula can be expressed as 
jk ij ik
w a a
k (k 1)
It is obvious that the formula reduces to the unweighted (3) when jkw  are substituted 
by jka . 
3. Onnela et. al. (2005) 
ij jk ki
w w w
k (k 1)
reduces to the unweighted definition (3) when jkw  are substituted by jka . 
ij ij(a ) a= , hence ( ) ( )
ij jk ki ij jk ki ij jk kiw w w a a a a a a= =  
ij jk ki ij jk ki
j k j kO
i i i i
a a a a a a
k (k 1) k (k 1)
∑∑ ∑∑
4. Barrat et. al. (2004) 
ij ikB
w,i ij jk ki
j,ki i
C a a a
s (k 1) 2
− ∑  
reduces to the unweighted definition (3) when ijw  and ikw  are substituted by the 
adjacency matrix elements. 
 i ij ij i
j (i) j (i)
s w a k
∈Π ∈Π
= = =∑ ∑  and  2ij ija a= . 
ij ik ij ij jk ki ik ij jk kiB
w,i ij jk ki
j,k j,ki i i i
ij jk ki ij jk ki ij jk ki ij jk ki
j,k j,ki i i i
ij jk ki
j,ki i
a a a a a a a a a a1 1
C a a a
k (k 1) 2 k (k 1) 2
a a a a a a a a a a a a1 1
k (k 1) 2 k (k 1) 2
a a a
k (k 1)
= = =
= = =
5. Serrano et. al. (2006) formula can be expressed as 
ij ik kj ij ik kj ij ik kj ij ik kj
j k j k j k j kS
w,i 2 2 22
i i i ij2 2ij2
ji ij2i
jij i
ij ik kj
ij ij
w w a w w a w w a w w a
s (1 Y ) s w1w s 1 ws 1 ss
w w a
= = = = =
− −⎛ ⎞ ⎛ ⎞⎛ ⎞ −⎜ ⎟ ⎜ ⎟− ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠⎝ ⎠
∑∑ ∑∑ ∑∑ ∑∑
It is obvious that the formula reduces to the unweighted (3) when jkw  are substituted 
by jka . 
APPENDIX B. The values of the weighted clustering coefficients of some node i 
participating in a fully connected triangle. 
We calculate the weighted clustering coefficient of node 1. 
1. Zhang et. al. (2005) 
3 3 3
1j jk k1 1j j2 21 j3 31
j 1 k 1 j 1Z
w,1 2 2 2 23 3
12 13 12 132
1j 1j
j 1 j 1
12 23 31 13 32 21 12 23 31
12 13 12 13
w w w w w w w w
w w w w
w w w w w w 2w w w
         w
2w w 2w w
≠ ≠ ≠
= = =
+ − −⎛ ⎞
= = =
∑ ∑  
2. Lopez-Fernandez et. al. (2004) 
3 3 3
jk 2k 3k
k 1 j 1L 23 32k 1
w,1 23
w w w
k (k 1) 2(2 1) 2
≠ ≠ ≠
= = = =
3. Onnela et. al. (2005) 
( ) ( ) ( )
( ) ( ) ( )
( ) ( ) ( ) ( )
( ) ( ) ( )
1 1 1
3 3 3
1j jk k1 1j jk k1
j k j kO
1 1 1
3 3 3
1j j2 21 j3 31
1 1 1 1
3 3 3 3
12 23 31 13 32 21
1 1 1
3 3 3
12 23 31 13 32 21 12 23 31
w w w w w w
k (k 1) 2(2 1)
         w w w w w
w w w w w w
w w w w w w w w w 1
= = =
= + =⎜ ⎟
= + =⎢ ⎥⎣ ⎦
= + = ≤⎢ ⎥⎣ ⎦
∑∑ ∑ ∑
O O O 3
w,1 w,2 w,3 12 23 31C  C C w w w 1= = = ≤  
4. Barrat et. al. (2004) 
ij ikB
w,i ij jk ki
j,ki i
C a a a
s (k 1) 2
− ∑  
 Degree of node 1: 1 1j
j (1)
k a 2
= =∑  
 Strength of node 1: 1 1j 12 13
j (1)
s w w w
= = +∑  
1j 1kB
w,1 1j jk k1
j,k1 1
1j 12 1j 13
1j j2 21 1j j3 31
13 12 12 13
13 32 21 12 23 31
12 13 12 23 31 12 23 31
12 13
C a a a
s (k 1) 2
w w w w1
a a a a a a
s (2 1) 2 2
w w w w1
a a a a a a
s 2 2
w w a a a a a a 1
+ +⎛ ⎞
= + =⎜ ⎟− ⎝ ⎠
+ +⎛ ⎞
= + =⎜ ⎟
= + = =
since 12 23 31a a a 1= = =  
We also prove that Barrat et. al. definition for the weighted clustering coefficient is 
independent of all weights for all fully connected networks. 
ij ih ij ihB
w,i ij jh hi ij jh hi
j,h j hi i i i
ik ihi1 ih i2 ih
i1 1h hi i2 2h hi ik k h hi
i1 i1 i2 i1
i1 11 1i i2 21 1i
w w w w1 1
C a a a a a a
s (k 1) 2 s (k 1) 2
w ww w w w1
a a a a a a ... a a a
s (k 1) 2 2 2
w w w w1
a a a a a a .
s (k 1) 2 2
= = =
+⎛ ⎞+ +
= + + + =⎜ ⎟
− ⎝ ⎠
= + +
i i i i
i i i i i i
ik i1
ik k 1 1i
ik i2i1 i2 i2 i2
i1 12 2i i2 22 2i ik k 2 2i
i1 ik i2 ik ik ik
i1 1k k i i2 2k k i ik k
.. a a a
w ww w w w
                a a a a a a ... a a a ...
2 2 2
w w w w w w
               a a a a a a ... a a
2 2 2
⎡ +⎛ ⎞
+ +⎢⎜ ⎟
+⎛ ⎞+ +
+ + + + + +⎜ ⎟
+ + +
+ + + +
i ik k i
For a fully connected network: ij ia 1, i, j 1, 2,..., k= ∀ =  and iia 0= , so 
ik i1B i2 i1
ik i2i1 i2
i1 ik i2 ik
w ww w1
C        0        ...
s (k 1) 2 2
w ww w
                                  0      ... ...
w w w w
                          ...       0  
⎡ +⎛ ⎞+
= + + + +⎢⎜ ⎟
− ⎝ ⎠⎣
+⎛ ⎞+
+ + + + + +⎜ ⎟
+ + + +
i1 i2 ik i1
i1 i2 ik i2
i1 i2 ik ik
     
w w ... w w1
             k 2
s (k 1) 2 2
w w ... w w
                              k 2 ...
w w ... w w
                              k 2
=⎥⎜ ⎟
⎡ + + +⎛ ⎞
= + − +⎢⎜ ⎟
− ⎝ ⎠⎣
+ + +⎛ ⎞
+ + − + +⎜ ⎟
+ + +
+ + −
i1 i2 ik i1 i2 ik
i1 i2 ik i1 i2 ik
i i i
w w ... w w w ... w1
        k   k 2
s (k 1) 2 2
w w ... w w w ... w1
        2k 2 1
s (k 1) 2 s
=⎥⎜ ⎟
+ + + + + +⎛ ⎞
= + − =⎜ ⎟
− ⎝ ⎠
+ + + + + +
= − = =
since 
ii i1 i2 ik
s w w ... w= + + +  
5. Serrano et. al. (2006) 
3 3 3
1j jk k1 1j j2 21 j3 31
j 1 k 1 j 1S
w,1 2 2 2 23 3
12 13 12 132
1j 1j
j 1 j 1
12 23 31 13 32 21 12 23 31
12 13 12 13
w a w w a w a w
w w w w
w a w w a w 2w a w
         1
2w w 2w w
≠ ≠ ≠
= = =
+ − −⎛ ⎞
= = =
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