J. Software Engineering & Applications, 2010, 3, 1080-1087
doi:10.4236/jsea.2010.311127 Published Online November 2010 ( ht t p : / /www.SciRP.org/journal/jsea)
Copyright © 2010 SciRes. JSEA
Research and Analysis of Structural Hole and
Matching Coefficient
Penghua Cai, Hai Zhao, Hong Liu, Rong Pan, Zheng Liu, Hui Li
Department of Information Science and Engineeri n g , Northeast University, Shenyang, China.
Email: caipenghua2008@yahoo.cn
Received August 28th, 2010; revised September 18th, accepted September 23rd, 2010.
Measure is a map from the reality or experimental world to the mathematical world, through which people can more
easily understand the properties of entities and the relationship between them. But the traditional software measure-
ment methods have been unable to effectively measure this large-scale software. Therefore, trustworthy measurement
gives an accurate measurement to these emerging features, providing valuable perspectives and different research di-
mensions to understand software systems. The paper introduces the complex network theory to software measurement
methods and proposes a statistical measurement methodology. First we study the basic parameters of the complex net-
work, and then introduce two new measur ement parameters: structural holes, matching coefficient.
Keywords: Large-Scale Software, Trustworthy Measurement, Structural Holes, Matching Coefficient
1. Introduction
Now large software network is increasingly showing
“small world” and “scale-free”-characteristics of com-
plex networks. The results of studying complex networks
provide strong support for people to explore characteris-
tics of the overall structure of large-scale software net-
work [1,2]. Using a network view research the software
network, this has been recognized by more and more
researchers. The traditional measurement methods focus
on the micro-level statistics and only do some aspects of
the software evaluation because of lacking parameters.
Therefore, the paper imports complex network theory
into the traditional measurement methods and introduces
some new metrics to measure the different characteristics
of the software from the different levels. This paper also
puts forward a measurement methodology, which make
the basic intrinsic property and the overall measures
properties of software as the core and use multiple meas-
urement parameters (the basic parameters in complex
network, the newly introduced metric) to measure some
important characteristics and structural features, provid-
ing an importan t basis for measuring software quality.
2. Structural Hole
1) The theory of structural hole
The concept of structural holes is from the social
structure of competition [3]. It is form social network
research. In brief, structural holes are the relationship
between the two non-duplicate persons. In Figure 1, we
use software network formed by four nodes A, B, C, D to
illustrate structural hole. In the left picture A has three
structural holes (BC, BD and CD); because the three
nodes B, C, D have no direct connection and only node
A is associated with these three classes. Compared with
other three nodes, node A has competitive advantage. It
is in the center, so most likely close to all the nodes in
the software network. The right picture is actually a
closed network, so there is no structural hole.
Figure 1 shows two extreme cases of structural hole in
the small-scale software network: the whole-hole struc-
ture network and no- hole structure network. In the actual
software, it has three types of structure as following:
Figure 1. Examples of structural holes.
Research and Analysis of Structural Hole and Matching Coefficient 1081
Any node in the software network has direct contact
with other nodes. From the whole network view, it is
“no-hole” structure. This structure only exists in small-
scale software network and such groups are actually
closed, so the importance of each node in the networks is
basically equal. There are many nodes needed to be up-
dated. It is difficu lt to control them and update software.
In addition, the cost of maintaining this high redundancy
network is high.
Only the central node has direct link with every other
node in the network. The other nodes do not connect
with every node directly. From the whole view of the
network, the phenomenon of no direct contact or rela-
tionship breaking off is structural holes. There are no
direct connections among the rest nodes, which is
whole-hole structure.
2) The algorithm of structural holes
In the aspects of structural holes measurement, struc-
tural constraint algorithm and betweenness centrality
algorithm have been used. Structural constraint algorithm
uses closeness among nodes as measure targets, depend-
ence among nodes as the evaluation criteria. It can de-
termine the degree of software network structural holes.
At the same time if nodes across more structural holes,
they have less redundant connections, can access more
non-redundant in formation and are used more frequently.
Betweenness centrality algorithm largely determines the
centering level of the nodes. Therefore, the paper uses
structural constraint algorithm to compute structural
Definition 2.1 Network Constraint index: This index
describes direct or indirect closeness between a node and
other nodes. If the network Constraint index is higher,
the network is closer and the structural holes are fewer.
The concrete calculating steps is as follows [4]:
ij ji
ij ik ki
is the ratio of the shortest path length between
node i and node j to the sum of the shortest path length
about all the neighboring nodes of node i. is the
shortest pat h length bet ween node i and node j. ij
ijijik kj
kk ikj
cp pp
ij is the binding level between node i and node j.
When node j is the only adjacent node of node i, ij
gets maximal value 1.When node j is indirectly con-
nected with node i through other nodes, ij gets mini-
mum value.Node k is the adjacent node of node i.
By formula (2) and formula (3) we can calculate net-
work constraint index of node i.
Structural holes are used to describe a node in de-
pendence on other nodes. Few structural holes show
strong dependence on other nodes. Network constraint
index is the quantization of structural ho les. By calculat-
ing the network constraint index of structural holes, we
can understand the degree of structural holes in the soft-
ware network.
3. Matching Coefficient
In 2002, Newman put forward another important statis-
tical parameter used to mark the network, which is as-
sortativity. Assortativity is represented by r. It is chang-
ing between –1 and 1 that means nodes are prior to es-
tablish side connection with similar nodes in the network
[5,6]. When r is greater than zero, nodes are prior to con-
nect with similar nodes. Such network is called assorta-
tive mixing. When r is less than zero, nodes are prior to
connect with dissimilar nodes. Such network is called
disassortative m i xi ng.
Definition 3.1 assortative coefficient: Incidence rela-
tion between nodes in the network can be described by
assortative coefficient [7,8]:
()[ ()
iii i
ii ii
EjkE jk
 
i and i are the degree of the i side’s two vertices.
E is the number of sides in the network.
j k
If assortative coefficient is greater than 0, the network
is assortative mixing; if assortative coefficient is less
than 0, the network is disassortative mixing; if assorta-
tive coefficient is equal to 0, the network is randomized.
Assortative coefficient reflects the connectivity of net-
work nodes. In the assortative mixing network, nodes of
a high degree tend to connect with nodes of a high de-
gree. In the disassortative mixing network, nodes of a
high degree tend to connect with nodes of a low degree.
In Figure 2, it is a network composed by 10 nodes. In
Figure 2(a), 0.372881r
. Node 1’s degree is 5,
which is a high degree node and connect with nodes (de-
gree is 2 or 1). Such network is disassortative mixing. In
Figure 2(b),
. Degrees of all nodes are similar, that
is assortative mixing.
4. The Law and Analysis of Metrics in the
Network Software
4.1. Correlation Analysis of Degree and
Structural Holes
Degree is used to describe the connected complexity of a
opyright © 2010 SciRes. JSEA
Research and Analysis of Structural Hole and Matching Coefficient
Copyright © 2010 SciRes. JSEA
Table 1. The statistical characteristics of 4 kinds of software
number of
number of
Quartz 255 63 231 1.81176
Abiword 1712 203 2484 2.84211
Mozilla 8354 1159 13581 3.32248
Eclipse 14730 1721 27560 3.74202
node and its neighboring nodes. The larger value of a
node degree, the more important it shows, but not for
chain network. Structural holes are used to show the im-
portance of a node from another point.
First we analyze the network structure of four ob-
ject-oriented networks (Quartz, Abiword, Mozilla and
Eclipse), as shown in Table1. As can be seen from Table
1, the scales of them vary widely. Compared with total
nodes, isolated nodes were few. So the four software
Figure 2. Examples of assortative mixing and disassortative
(a) Quartz (b) Abiword
(c) Mozilla (d) Eclipse
Figure 3. Diagram of the distribution of network constraint index and degree.
Research and Analysis of Structural Hole and Matching Coefficient 1083
networks have representativeness in all software samples.
The paper analyzes interdependency of degree and
structural holes about these four software network. As
the structural holes are quantified through network con-
straint index, so interdependency of degree and structural
holes is also interdependency of degree and network
constraint index. In Figure 3, horizontal ordinate is the
value of every node’s degree, vertical coordinates is the
value of network constraint index. In all software net-
work, the greater the value of nodes degree, the smaller
network constraint index, the more structural holes, the
weaker dependency on the around nodes. A special case
is that a node’s degree is 0 and its network constraint
index is 1, then the node does not have structural holes. It
is isolated node. In the software network it will not be
called by other operations.
Since isolated nodes do not affect the software feature,
after removing isolated nodes we make curve fitting to
the relationship of degree and network constraint index.
In Figure 4, horizontal ordinate is the value of node’s
degree, vertical coordinates is the value of network con-
straint index.
Relationship distribution curve of structural holes and
network constraint index is power curve, which shows an
important feature of software system modularization.
Fitting curve is the mathematical expression of this fea-
ture. For example, Software Network Quartz’s fitted
power function equation is as follows:
 (5)
X is the nodes’ degree value (abscissa). These four
software network’ parameter estimates are shown in Ta-
ble 2. In software network, the greater the value of nodes
degree, the smaller network constraint index, the more
structural holes.
Whether a regression model is good or not, the most
commonly used index is the coefficient of determination
[9,10]. The index is based on the decomposition of the
dispersion quadratic sum. Coefficient of determination is
a comprehensive measure for regression model’s good-
ness of fit [11, 12].
(a) Quartz (b) Abiword
(c) Mozilla (d) Eclipse
Figure 4. The fitness gr ap h o f relationship between network constraint index and degree.
opyright © 2010 SciRes. JSEA
Research and Analysis of Structural Hole and Matching Coefficient
Table 2. Model summary and parameter estimation.
model summary parameter estimate
system R F Sig. constant b1
Quartz .955 3995.527 .000 1.003 –918
Abiword .943 24989.658 .000 1.016 –892
Mozilla .917 79097.785 .000 .996 –882
Eclipse .960 313520.018 .000 1.004 –934
Formula of correlation coefficient:
() ()
 
Formula of determination coefficient:
Rr (7)
F test is mainly for variance analysis. Sig is result of F
test. If Sig is less-than 0.05, which declare that difference
is significant.
From Table 2 the coefficient of determination R =
0.958, Sig < 0.05. Therefore we can conclude goodness
of fit is very high and fitting power function can fully
reflect a power curve relationship between network con-
straint index and node degree. So fitting results is ac-
Through the four software networks we can see that
the structural holes obey specified rule. Enlarge sample,
and then test 200 software networks. The results are
shown in Figure 5. Abscissa is the software serial num-
ber. In Figure 5 vertical coordinates is the goodness of
fit; in Figure 5(b) vertical coordinates is the relation
fitting power function curve parameter estimates of net-
work constraint index and degree.
In Figure 5(a), goodness of fit of the network con-
straint index and the degree is between 0.80 and 0.98.
This shows that relationship of the network constraint
index and the degree apparently obeys power function
distribution. Of course, 25 software networks’ goodness
of fit is between 0.50 and 0.80, which indicate the net-
work constraint index and the degree are moderate cor-
relation. In addition, 3 software networks’ goodness of
fit is less than 0.5, which indicate th e network constraint
index and the degree are low correlation. In Figure 5(b),
power function relation of network constraint index and
degree changes little.
In software network, correlation of degree and struc-
tural holes contributes to analyze collaborative relation-
ships between different types of software entities. It is
useful to discover software entities’ problems. Complex
class or module are tend to be composed by relatively
simple class or module. This is the software constructiv-
ity principle. On the other hand, correlation between the
network constraint index and the degree of structural
Figure 5. Diagram of the distribution coefficient of etermi-
nation and parameter estimation.
holes is helpful to the analysis of system hierarchy and
modularity. Class or module with large degree are tend
to gather with class or module with small degree, that
shows a high cohesion
4.2. Law of Matching Coefficient
The paper makes a further analysis on the 200 samples
and calculates the matching coefficient for each software
network. The results are shown in Figure 6. In the 200
software networks, 80% of them are disassortative mix-
ing; 20% of them are assortative mixing.
First of all, we analyze the disassortative mixing soft-
ware network, because they occupy majority of the soft-
ware samples. Software network currently in use most
are disassortative mixing. From Table 3 we can conclude
that disassortative mixing software networks have no
opyright © 2010 SciRes. JSEA
Research and Analysis of Structural Hole and Matching Coefficient
Copyright © 2010 SciRes. JSEA
Figure 6. Diagram of the distribution of the mixing coeffi-
cient. (a) The number is less than 1000.
concern with the total number of nodes. The average
degree, the average structural holes of the disassortative
mixing software network don’t have obvious law. Some
software networks are well known and have higher
evaluation. Their coefficient of determination of struc-
tural holes and degree are grea t e r than 0. 8.
Some assortative mixing software networks are shown
in Table 4. As can be seen from Table 4, assortative
mixing software network is different from disassortative
mixing software network; moreover network constraint
index and degree goodness of fit is relatively low. The
number of assortative mixing software network’ nodes
are generally small, and matching coefficient has nothing
to do with the avera ge degree and structural holes.
By comparison, it is discovered that in the 200 software
networks, when the total number of nodes is more than
1,000, they are disassortative mixing software networks.
In these software networks, the nodes with lower degree
are considered as a relatively simple module in the soft-
ware network. In disassortative mixing software network,
nodes with high degree tend to connect with nodes with
low degree. The nodes with lower degree are conducive
to the decomposition of software tasks, while the nodes
with higher degree are key points for software modules
completing the complex task.
(b) The number is more than 1000.
Figure 7. Relationship between the mixing coefficient and
the number of nodes.
As the pressure of design and implementation, it is
necessary to keep each module simple and effective.
When a node has a high degree, it also has the features of
complexity and high multiplexing. In the assortative
mixing software network, if a node has a high degree or
connects with high degree nodes that will cause system
problems. System maintainability and modifiability fall
down. It is need to reconstruct for such modules
Compared Table 3 with Table 4, it can be concluded
that matching coefficient was correlated with the number
of nodes. The relationship between matching coefficient
and software size is shown in Figure 7. The abscissa is
the total number of each software network’s nodes, the
vertical coordinates is the matching coefficient. In Fig-
ure 7(a), the number of each software network’s nodes is
less than 1000. The majority of software network’s
matching coefficients are below 0. In Figure 7(b), the
number of each so ftware network’s nodes is greater than
1000. When the number of nodes is greater than 1000,
the software network is disassortative mixing.
5. Conclusion
The paper uses structural holes and the matching coef-
ficient to measure software. Software structural holes
measure the software network from the software de-
pendent features. Correlation between network constraint
index and the degree obey power law distribution that
reflects an important software feature. As the study of
the software network is still in the exploration stage, so
the study of measurement methods of software network
Research and Analysis of Structural Hole and Matching Coefficient
Table 3. Aisassortative mixing.
Software name matching coefficientnodesgoodness of fitaverage degreeaverage structural holes
kdegraphics-3.5.3 –0.121387 20140.931 3.32572 0.600915
mysql_6.0.6 –0.12225 37930.836 2.83048 0.709367
jEditR1.35 –0.125693 822 0.868 1.74696 0.815401
kdebase-3.5.3 –0.126603 16770.879 2.12165 0.745993
kdevelop-3.4.0 –0.128954 14530.917 1.97385 0.77426
qhacc-3.4 –0.130487 148 0.932 3.22973 0.480221
rpm-4.4.1 –0.131246 12600.815 2.05397 0.781209
nss-3.9.2 –0.133362 910 0.849 3.17363 0.667572
freemind0.9.0 –0.13402 713 0.877 2.61711 0.714985
mysql_5.1.26 –0.136164 31940.843 2.57044 0.732296
sim-0.9.4 –0.138038 786 0.93 2.44275 0.72691
kicad-20060626 –0.139725 212 0.854 2.83019 0.694856
kopete-0.12.1 –0.140958 15120.891 2.65741 0.665513
qtiplot-0.8.2 –0.141222 166 0.958 1.83133 0.750994
kdeedu-3.5.4 –0.141509 10100.891 2.04158 0.765379
mysql_5.0.67 –0.142316 31330.921 2.45388 0.734618
mysql-5.0.56 –0.142425 31320.92 2.45019 0.735107
ArgoUML-0.26.2 –0.143707 20310.828 2.18316 0.805827
koffice-1.5.0 –0.143862 45800.915 2.57293 0.695893
glib-2.16.5 –0.144575 474 0.816 1.64979 0.828419
Table 4. Assortative mixing.
Software name matching coefficientnodesgoodness of fitaverage degreeaverage structural holes
gnuplot-4.0.0IDE 0.516592 93 0.509 2.02151 0.717183
exim-4.62 0.36244 114 0.337 1.21053 0.826957
courier-0.52.2 0.345491 376 0.528 2.00532 0.724346
freeradius-1.1.0 0.33909 170 0.529 1.38824 0.855368
maildrop-2.0.2 0.280709 107 0.447 1.79439 0.790998
ups-3.38 0.263252 246 0.43 1.72358 0.829059
coreutils-5.2.1 0.223548 93 0.466 1.2043 0.80448
nedit-5.5 0.207817 130 0.73 1.63077 0.79206
kdeartwork-3.5.4 0.199741 162 0.668 1.2963 0.847517
evince-0.5.4 0.180686 232 0.677 1.2069 0.860548
bash-3.2 0.163871 99 0.524 1.51515 0.797375
electric-7.00 0.143483 412 0.657 3.35437 0.635638
bibletime-1.6 0.0948479 159 0.896 2.03774 0.712981
freeradius-2.0.5 0.064476 218 0.685 1.6055 0.823288
strongswan-2.6.4 0.050583 312 0.722 1.69872 0.814492
evms-2.5.5 0.0442493 352 0.815 1.79545 0.790988
jabberd-2.0s11 0.0435754 117 0.73 3.65812 0.637675
glibc-2.3.6 0.0400406 961 0.668 1.26743 0.853633
dasher-4.0.4 0.0368399 213 0.84 2.83568 0.664322
cyrus-2.3.12 0.110243 279 0.643 1.94265 0.791312
is also in the exploration stage. This paper studies the
single property, association of the property and holistic
measure of the software. However, arising deviation is
inevitable in the process. As the constraints of time and
energy, the samples of this paper’s research are still
small samples. It is need to enlarge samples. Next we
need to further examine the effectiveness of measure-
ment methodology in the actual development project,
develop and integ rate auxiliary means to guide th e actual
software development.
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