Abstract
In this article, we tackle a challenging problem in quantitative graph theory. We establish relations between graph entropy measures representing the structural information content of networks. In particular, we prove formal relations between quantitative network measures based on Shannon's entropy to study the relatedness of those measures. In order to establish such information inequalities for graphs, we focus on graph entropy measures based on information functionals. To prove such relations, we use known graph classes whose instances have been proven useful in various scientific areas. Our results extend the foregoing work on information inequalities for graphs.
Introduction
Complexity is an intricate and versatile concept that is associated with the design and configuration of any system [1], [2]. For example, complexity can be measured and characterized by quantitative measures often called indices [3]–[5]. When studying the concept of complexity, information theory has been playing a pioneering and leading role. Prominent examples are the theory of communication and applied physics where the famous Shannon entropy [6] has extensively been used. To study issues of complexity in natural sciences and, in particular, the influence and use of information theory, see [7].
In this paper, we deal with an important aspect when studying the complexity of network-based systems. In particular, we establish relations between information-theoretic complexity measures [3], [8]–[11]. Recall that such entropic measures have been used to quantify the information content of the underlying networks [8], [12]. Generally, this relates to exploring the complexity of a graph by taking its structural features into account. Note that numerous measures have been developed to study the structural complexity of graphs [5], [8], [13]–[22]. Further, the use and ability of the measures has been demonstrated by solving interdisciplinary problems. As a result, such studies have led to a vast number of contributions dealing with the analysis of complex systems by means of information-theoretic measures, see, e.g., [8], [13]–[22]. Figure 1 shows a classification scheme of quantitative network measures exemplarily.
Figure 1. A classification of quantitative network measures.
The main contribution of this paper is to study relations between entropy measures. We will tackle this problem by means of inequalities involving network information measures. In particular, we study so-called implicit information inequalities which have been introduced by Dehmer et al. [23], [24] for studying graph entropies using information functionals. Generally, an implicit information inequality involves information measures which are present on either side of the inequality. It is important to emphasize that relatively little work has been done to investigate relations between network measures. A classical contribution in this area is due to Bonchev et al. [25]. Here, the relatedness between information-theoretic network measures has been investigated to detect branching in chemical networks. Further, implicit information inequalities have been studied for hierarchical graphs which turned out to be useful in network biology [26].
We first present closed form expressions of graph entropies using the graph classes, stars and path graphs. Further, we infer novel information inequalities for the measures based on the 
-sphere functional. The section “Implicit Information Inequalities” presents our main results on novel implicit inequalities for networks. We conclude the paper with a summary and some open problems. Before discussing our results, we will first present the information-theoretic measures that we want to investigate in this paper.
Methods
In this section, we briefly state the concrete definitions of the information-theoretic complexity measures that are used for characterizing complex network structures [3], [6], [9], [27]. Here we state measures based on two major classifications namely partition-based and partition-independent measures and deal mainly with the latter.
Given a simple, undirected graph 
, let 
denote the distance between two vertices 
and 
, and let 
. Let 
denote the 
-sphere of a vertex 
defined as 
. Throughout this article, a graph 
represents a simple undirected graph.
Definition 1
Let
be a graph on
vertices and let
be a graph invariant of
. Let
be an equivalence relation that partitions
into
subsets
, with cardinality
for
. The total structural information content of
is given by
 |
(1) |
Definition 2
Let
be a graph on
vertices and let
, for
be the probability value for each partition. The mean information content of
is
 |
(2) |
In the context of theory of communication, the above equation is called as Shannon equation of information [28].
Definition 3
Let
be a graph on
vertices. The quantity
 |
(3) |
is a probability value of
. 
is an arbitrary information functional that maps a set of vertices to the non-negative real numbers.
Remark 1
Observe that,
defines a probability distribution over the set of vertices as it satisfies
, for every vertex
, 
and
.
Using the resulting probability distribution associated with 
leads to families of network information measures [3], [9].
Definition 4
The graph entropy of
given representing its structural information content:
 |
(4) |
In order to define concrete graph entropies, we reproduce the definitions of some information functionals based on metrical properties of graphs [3], [9], [27].
Definition 5
Parameterized exponential information functional using
-spheres:
 |
(5) |
where 
and 
for 
.
Definition 6
Parameterized linear information functional using
-spheres:
 |
(6) |
where 
for 
.
Remark 2
Observe that, when either
or the
are all equal, the functional
and
becomes a constant function and, hence, the probability on all the vertices are equal. That is
, for
. Thus, the value of the entropy attains its maximum value,
. Thus, in all our proofs, we only consider the non-trivial case, namely
and/or at least for two coefficients holds
.
Next, we will define the local information graph to use local centrality measures from [9]. Let 
be the subgraph induced by the shortest path starting from the vertex 
to all the vertices at distance 
in 
. Then, 
is called the local information graph regarding
with respect to
, see [9]. A local centrality measure that can be applied to determine the structural information content of a network [9] is then defined as follows.
Definition 7 The closeness centrality of the local information graph is defined by
 |
(7) |
Remark 3 Note that centrality is an important concept that has been introduced for analyzing social networks [29], [30] . Many centrality measures have been contributed [30] , and in particular, various definitions for closeness centrality [30]–[32] . We remark that the above definition has been firstly defined by Sabidussi [31] for arbitrary graphs. However, we use the measure as a local invariant defined on the subgraphs induced by the local information graph [9] .
Similar to the 
-sphere functionals, we define further functionals based on the local centrality measure as follows.
Definition 8 Parameterized exponential information functional using local centrality measure:
 |
(8) |
where 
, 
for 
.
Definition 9 Parameterized linear information functional using local centrality measure:
 |
(9) |
where 
, for 
.
Note that the coefficients 
can be chosen arbitrarily. However, the functionals become more meaningful when we choose the coefficients to emphasize certain structural characteristics of the underlying graphs. Also, this remark implies that the notion of graph entropy is not unique because each measure takes different structural features into account. Further, this can be understood by the fact that a vast number of entropy measures have been developed so far. Importantly, we point out that the measures we explore in this paper are notably different to the notion of graph entropy introduced by Körner [21]. The graph entropy due to Körner [21] is rooted in information theory and based on the known stable set problem. To study more related work, survey papers on graph entropy measures have been authored by Dehmer et al. [3] and Simonyi [33].
Results and Discussion
Closed Form Expressions and Explicit Information Inequalities
When calculating the structural information content of graphs, it is evident that the determination of closed form expressions using arbitrary networks is critical. In this section, we consider simple graphs namely trees with smallest and largest diameter and compute the measures defined in the previous section. By using arbitrary connected graphs, we also derive explicit information inequalities using the measures based on information functionals (stated in the previous section).
Stars
Star graphs, 
, have been of considerable interest because they represent trees with smallest possible diameter (
) among all trees on 
vertices.
Now, we present closed form expressions for the graph entropy by using star graphs. For this, we apply the information-theoretic measures based on information functionals defined in the preliminaries section.
Theorem 4
Let
be a star on
vertices. Let
be the information functionals as defined before. The information measure is given by
 |
(10) |
where
is the probability of the central vertex of
:
 |
(11) |
if 
.
 |
(12) |
if 
.
 |
(13) |
if 
.
 |
(14) |
if 
.
Proof:
Consider
, where 
and 
for 
.
We get,
 |
(15) |
Therefore,
 |
(16) |
Hence,
 |
(17) |
By substituting the value of 
in 
and simplifying, we get
 |
Consider
, where 
for 
.
We get,
 |
(18) |
Therefore,
 |
(19) |
Hence,
 |
(20) |
By substituting the value of 
in 
and simplifying, we get
 |
Consider the case
, where 
, 
for 
.
 |
(21) |
denotes the closeness centrality measure.
Then, we yield
 |
(22) |
Therefore,
 |
(23) |
Hence,
 |
(24) |
By substituting the value of 
in 
and simplifying, we obtain
 |
where 
.
- Consider
, where 
for 
. 
is defined via Equation (18). We get,
Therefore,
(25)
Thus,
(26)
By substituting the value of
(27) 
in 
and simplifying, we get
where
(28) 
. 
By choosing particular values for the parameters involved, we get concrete measures using the above stated functionals. For example, consider the functional 
and set
 |
(29) |
If we plug in those values in Equations (10) and (11), we easily derive
 |
(30) |
Paths
Let 
be the path graph on 
vertices. Path graphs are the only trees with maximum diameter among all the trees on 
vertices, i.e., 
. We remark that to compute a closed form expression even for path graphs, is not always simple. To illustrate this, we present the concrete information measure 
by choosing particular values for its coefficients.
Lemma 5
Let
be a path graph and consider the functional
defined by
Equation (6)
. We set
, 
. We yield
 |
(31) |
Proof: Let 
be a path graph trivially labeled by 
, 
(from left to right).
Given 
with 
for 
.
By computing 
, when 
, for 
, we infer
 |
(32) |
 |
(33) |
 |
(34) |
Therefore,
 |
(35) |
and, hence,
 |
(36) |
where 
, for 
. By substituting these quantities into 
yields the desired result.
Note that when using the same measure with arbitrary coefficients, its computation is intricate. In this regard, we present explicit bounds or information inequalities for any connected graph if the measure is based on the information functional using 
-spheres. That is, either 
or 
.
General connected graphs
Theorem 6
Given any connected graph
on
vertices and let
given by
Equation (5)
. Then, we infer the following bounds:
 |
(37) |
 |
(38) |
 |
(38) |
 |
(40) |
 |
(41) |
Proof: Consider 
, where 
and 
for 
. Let 
and 
. Recall (see Remark (2)) that, when either 
or when all the coefficients (
) are equal, the information functional becomes constant and, hence, the value of 
equals 
. In the following, we will discuss the cases 
and 
, and we also assume that not all 
are equal.
Case 1:
: We first construct the bounds for 
as shown below:
 |
(42) |
 |
(43) |
Similarly,
 |
(44) |
Therefore, from the Equations (43) and (44), we get
 |
(45) |
Hence,
 |
(46) |
Let 
. Then, the last inequality can be rewritten as,
 |
(47) |
Upper bound for 
:
Since 
and 
, we have 
. Hence, we have 
and 
. Thus we get,
 |
(48) |
By adding over all the vertices of 
, we obtain
 |
(49) |
Lower bound for
:
We have to distinguish two cases, either 
or 
.
Case 1.1:
. We yield 
. Therefore,
 |
(50) |
By adding over all the vertices of 
, we get
 |
(51) |
Case 1.2:
.
In this case, we obtain 
and 
. Therefore, by using these bounds in Equation (4), we infer 
.
Case 2:
:
Consider Equation (42). We get the following bounds for 
:
 |
(52) |
Therefore,
 |
(53) |
Hence,
 |
(54) |
Set 
. Then, the last inequality can be rewritten as,
 |
(55) |
Upper bound for 
:
Since 
and 
, we have 
. Hence, we have 
and 
. Thus, we obtain,
 |
(56) |
By adding over all the vertices of 
, we get
 |
(57) |
Lower bound for 
:
Again, we consider two cases, either 
or 
.
Case 2.1:
.
In this case, we have 
and 
. Therefore, by substituting these bounds in the Equation (4), we obtain 
.
Case 2.2:
.
We have 
. Therefore,
 |
(58) |
By adding over all the vertices of 
, we get
 |
(59) |
Hence, the theorem follows.
In the next theorem, we obtain explicit bounds when using the information functional given by Equation (6).
Theorem 7
Given any connected graph
on
vertices and let
be given as in
Equation (6)
. We yield
 |
(60) |
 |
(61) |
 |
(62) |
 |
(63) |
Proof: Consider 
, where 
for 
. Let 
and 
. We have,
 |
(64) |
Similarly,
 |
(65) |
Therefore, from the Equations (64) and (65), we get
 |
(66) |
Hence,
 |
(67) |
Upper bound for 
:
Since 
, we have 
and 
. Hence,
 |
(68) |
By adding over all the vertices of 
, we obtain
 |
(69) |
Lower bound for 
:
Let us distinguish two cases:
Case 1:
.
We have 
and 
. Therefore, by applying these bounds to Equation (4), we obtain 
.
Case 2:
.
In this case, we have 
. Therefore,
 |
(70) |
By adding over all the vertices of 
, we obtain the lower bound for 
given by
 |
(71) |
Hence, the theorem follows.
Implicit Information Inequalities
Information inequalities describe relations between information measures for graphs. An implicit information inequality is a special type of an information inequality where the entropy of the graph is estimated by a quantity that contains another graph entropy expression. In this section, we will present some implicit information inequalities for entropy measures based on information functionals. In this direction, a first attempt has been done by Dehmer et al. [23], [24], [26]. Note that Dehmer et al. [23], [26] started from certain conditions on the probabilities when two different information functionals 
and 
are given. In contrast, we start from certain assumptions which the functionals themselves should satisfy and, finally, derive novel implicit inequalities. Now, given any graph 
. Let 
and 
be two mean information measures of 
defined using the information functionals 
and 
respectively. Let us further define another functional 
, 
. In the following, we will study the relation between the information measure 
and the measures 
and 
.
Theorem 8
Suppose
, for all
, then the information measure
can be bounded by
and
as follows:
 |
(72) |
 |
(73) |
where 
, 
, and
.
Proof: Given 
. Let 
and 
. Therefore 
. The information measures of 
with respect to 
and 
are given by
 |
(74) |
where 
 |
(75) |
where 
.
Now consider the probabilities,
 |
(76) |
 |
(77) |
 |
(78) |
Using Equation (77) and based on the fact that 
, we get
 |
(79) |
Thus,
 |
(80) |
and
 |
(81) |
Since the last term in the above inequality is positive, we get
 |
(82) |
By adding up the above inequalities over all the vertices of 
, we get the desired upper bound. From Equation (77), we also get a lower bound for 
, given by
 |
(83) |
Now proceeding as before with the above inequality for 
, we obtain
 |
(84) |
 |
(85) |
By using the concavity property of the logarithm, that is, 
, we yield
 |
(86) |
By adding the above inequality over all the vertices of 
, we get the desired lower bound. This proves the theorem.
Corollary 9
The information measure
, for
, is bounded by
and
as follows:
 |
(87) |
 |
(88) |
Proof: Set 
in Theorem (8), then the corollary follows.
Corollary 10
Given two information functionals,
, 
such that
, 
. Then
 |
(89) |
Proof: Follows from Corollary (9).
The next theorem gives another bound for 
in terms of both 
and 
by using the concavity property of the logarithmic function.
Theorem 11
Let
and
be two arbitrary functionals defined on a graph
. If
for all
, we infer
 |
(90) |
and
 |
(91) |
where
, 
and
.
Proof: Starting from the quantities for 
based on Equation (77), we obtain
 |
(92) |
 |
(93) |
 |
(94) |
 |
(95) |
Since each of the last two terms in Equation (95) is positive, we get a lower bound for 
, given by
 |
(96) |
Applying the last inequality to Equation (4), we get the upper bound as given in Equation (91). By further applying the inequality 
to Equation (95), we get an upper bound for 
, given by
 |
(97) |
Therefore,
 |
(98) |
Finally, we now apply this inequality to Equation (4) and get the lower bound as given in Equation (90).
The next theorem is a straightforward extension of the previous statement. Here, an information functional is expressed as a linear combination of 
arbitrary information functionals.
Theorem 12
Let
and
be arbitrary functionals defined on a graph
. 
are the corresponding information contents. If
for all
, we infer
 |
(99) |
and
 |
(100) |
where
, 
for 
. 
Union of Graphs
In this section, we determine the entropy of the union of two graphs. Let 
and 
be two arbitrary connected graphs on 
and 
vertices, respectively. Let 
be an information functional defined on these graphs denoted by 
, 
and let 
and 
be the information measures on 
and 
respectively.
Theorem 13
Let
be the disjoint union of the graphs
and
. Let
be an arbitrary information functional. The information measure
can be expressed in terms of
and
as follows:
 |
(101) |
where
with
and
.
Proof: Let 
be the given information functional. Let 
and 
. The information measures of 
and 
are given as follows:
 |
(102) |
where 
, and
 |
(103) |
 |
 |
(104) |
Hence,
 |
(105) |
 |
(106) |
 |
(107) |
Using these quantities to determine 
, we obtain
 |
(108) |
and
 |
(109) |
Upon simplification, we get the desired result.
Also, we immediately obtain a generalization of the previous theorem by taking 
-disjoint graphs into account.
Theorem 14
Let
, 
be
arbitrary connected graphs on
vertices, respectively. Let
be an information functional defined on these graphs denoted by
. Let
be the disjoint union of the graphs
for
. The information measure
can be expressed in terms of
, 
as follows:
 |
(110) |
where
with
for
.
Join of Graphs
Let 
and 
be two arbitrary connected graphs on 
and 
vertices, respectively. The join of the graphs 
is defined as the graph 
with vertex set 
and the edge set 
. Let 
be the information functional (given by Equation (5)) based on the 
-sphere functional (exponential) defined on these graphs and denoted by 
, 
. Let 
and 
be the information measures on 
and 
respectively.
Theorem 15
Let
be the join of the graphs
and
with
vertices. The information measure
can then be expressed in terms of
and
as follows:
 |
(111) |
where
for
, 
and
with
and
.
Proof: Let 
be the join of two connected graphs 
and 
. Here, 
. Let 
be the information functional defined by using the 
-sphere functional on 
. Let 
and 
. The information measures of 
and 
are given as follows:
 |
(112) |
 |
 |
(113) |
 |
 |
(114) |
 |
(115) |
Hence,
 |
(116) |
 |
(117) |
 |
(118) |
Using those entities to determine 
, we infer
 |
(119) |
and
 |
(120) |
Upon simplification, we get the desired result.
If we consider the linear 
-sphere functional 
(see Equation (6)), to infer an exact expression for the join of two graphs as in Theorem (15) is an intricate problem. By Theorem (16) and Theorem (17), we will now present different bounds in terms of 
and 
.
Theorem 16
Let
be the join of the graphs
and
on
vertices. Then, we yield
 |
(121) |
where
for
, 
and
with
and
.
Proof: Let 
and 
. The information measures of 
and 
are given as follows:
 |
(122) |
 |
 |
(123) |
 |
(124) |
 |
(125) |
Hence,
 |
(126) |
 |
(127) |
 |
(128) |
Since 
and 
are positive, we get a lower bound for 
given as
 |
(129) |
To infer a lower bound for the information measure 
, we start from the Equations (128), (129) and obtain
 |
(130) |
 |
(131) |
 |
(132) |
By using the inequality 
and performing simplification steps, we get,
 |
(133) |
By adding up the above inequality system (across all the vertices of 
) and by simplifying, we get the desired lower bound.
Further, an alternate set of bounds can be achieved as follows.
Theorem 17
Let
be the join of the graphs
and
on
vertices. Then, we infer
 |
(134) |
and
 |
(135) |
where
for
, 
and
with
and
.
Proof: Starting from Theorem (16), consider the value of 
given by Equation (128). By using the quantities for 
to calculate 
, we get
 |
(136) |
and
 |
(137) |
By simplifying and performing summation, we get
 |
(138) |
An upper bound for the measure 
can be derived as follows:
 |
(139) |
since each of the remaining terms in Equation (138) is positive. Finally, we infer the lower bound for 
as follows. By applying inequality 
to Equation (138), we get
 |
(140) |
Upon simplification, we get
 |
(141) |
Putting Inequality (139) and Inequality (141) together finishes the proof of the theorem.
Summary and Conclusion
In this article, we have investigated a challenging problem in quantitative graph theory namely to establish relations between graph entropy measures. Among the existing graph entropy measures, we have considered those entropies which are based on information functionals. It turned out that these measures have widely been applicable and useful when measuring the complexity of networks [3].
In general, to find relations between quantitative network measures is a daunting problem. The results could be used in various branches of science including mathematics, statistics, information theory, biology, chemistry and social sciences. Further, the determination of analytical relations between measures is of great practical importance when dealing with large scale networks. Also, relations involving quantitative network measures could be fruitful when determining the information content of large complex networks.
Note that our proof technique follows the one proposed in [23]. It is based on three main steps: Firstly, we compute the information functionals and in turn, we calculate the probability values for every vertex of the graph in question. Secondly, we start with certain conditions for the computed functionals and arrive at a system of inequalities. Thirdly, by adding up the corresponding inequality system, we obtain the desired implicit information inequality. Using this approach, we have inferred novel bounds by assuming certain information functionals. It is evident that further bounds could be inferred by taking novel information functionals into account. Further, we explored relations between the involved information measures for general connected graphs and for special classes of graphs such as stars, path graphs, union and join of graphs.
At this juncture, it is also relevant to compare the results proved in this paper with those proved in [23]. While we derived the implicit information inequalities by assuming certain properties for the functionals, the implicit information inequalities derived in [23] are based on certain conditions for the calculated vertex probabilities. Interestingly, note that by using Theorem (11) and Theorem (17), the range of the corresponding bounds is very small. We inferred that the difference between the upper and lower bound equals 
.
As noted earlier, relations between entropy-based measures for graphs have not been extensively explored so far. Apart from the results we have gained in this paper, we therefore state a few open problems as future work:
To find relations between
and 
, when 
is an induced subgraph of 
and 
is an arbitrary information functional.To find relations between
and 
, where 
, 
are so-called generalized trees, see [34]. Note that it is always possible to decompose an arbitrary, undirected graph into a set of generalized trees [34].To find relations between measures based on information functionals and the other classical graph measures.
To derive information inequalities for graph entropy measures using random graphs.
To derive statements to judge the quality of information inequalities.
Footnotes
Competing Interests: The authors have declared that no competing interests exist.
Funding: Matthias Dehmer and Lavanya Sivakumar thank the Austrian Science Fund (Project No. PN22029-N13) for supporting this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
- 1.Basak SC. Devillers J, Balaban AT, editors. Information-theoretic indices of neighborhood complexity and their applications. Topological Indices and Related Descriptors in QSAR and QSPAR, Gordon and Breach Science Publishers. 1999. pp. 563–595. Amsterdam, The Netherlands.
- 2.Wang J, Provan G. Zhou J, editor. Characterizing the structural complexity of real-world complex networks. 2009. pp. 1178–1189. Complex Sciences, Springer, Berlin/Heidelberg, Germany, volume 4 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
- 3.Dehmer M, Mowshowitz A. A history of graph entropy measures. Information Sciences. 2011;181:57–78. [Google Scholar]
- 4.Li M, Vitányi P. An Introduction to Kolmogorov Complexity and Its Applications. Springer; 1997. [Google Scholar]
- 5.Mowshowitz A. Entropy and the complexity of graphs: I. an index of the relative complexity of a graph. Bulletin of Mathematical Biophysics. 1968;30:175–204. doi: 10.1007/BF02476948. [DOI] [PubMed] [Google Scholar]
- 6.Shannon CE. A mathematical theory of communication. Bell System Technical Journal. 1948;27:379–423 and 623–656. [Google Scholar]
- 7.Bonchev D, Rouvray DH. Complexity in chemistry: Introduction and Fundamentals. Mathematical and Computational Chemistry 7. New York: CRC Press; 2003. [Google Scholar]
- 8.Bonchev D. Information Theoretic Indices for Characterization of Chemical Structures. 1983. Research Studies Press, Chichester.
- 9.Dehmer M. Information processing in complex networks: graph entropy and information functionals. Appl Math Comput. 2008;201:82–94. [Google Scholar]
- 10.Emmert-Streib F, Dehmer M. Information theoretic measures of UHG graphs with low computational complexity. Appl Math Comput. 2007;190:1783–1794. [Google Scholar]
- 11.Mehler A, Weiß P, Lücking A. A network model of interpersonal alignment. Entropy. 2010;12:1440–1483. [Google Scholar]
- 12.Bonchev D. Complexity in Chemistry. 2003. Introduction and Fundamentals. Taylor and Francis. Boca Raton, FL, USA.
- 13.Anand K, Bianconi G. Entropy measures for networks: Toward an information theory of complex topologies. Phys Rev E. 2009;80:045102. doi: 10.1103/PhysRevE.80.045102. [DOI] [PubMed] [Google Scholar]
- 14.Costa LdF, Rodrigues FA, Travieso G, Boas PRV. Characterization of complex networks: A survey of measurements. Advances in Physics. 2007;56:167–242. [Google Scholar]
- 15.Kim J, Wilhelm T. What is a complex graph? Physica A: Statistical Mechanics and its Applications. 2008;387:2637–2652. [Google Scholar]
- 16.Balaban AT, Balaban TS. New vertex invariants and topological indices of chemical graphs based on information on distances. Journal of Mathematical Chemistry. 1991;8:383–397. [Google Scholar]
- 17.Bertz SH. King R, editor. A mathematical model of complexity. 1983. pp. 206–221. Chemical applications of topology and graph theory, Elsevier, Amsterdam.
- 18.Basak SC, Magnuson VR, Niemi GJ, Regal RR. Determining structural similarity of chemicals using graph-theoretic indices. Discrete Applied Mathematics. 1988;19:17–44. [Google Scholar]
- 19.Bonchev D, Rouvray DH. Complexity in chemistry, biology, and ecology. Mathematical and Computational Chemistry. New York: Springer; 2005. pp xx+344. doi: http://dx.doi.org/10.1007/b136300. [Google Scholar]
- 20.Claussen JC. Offdiagonal complexity: A computationally quick complexity measure for graphs and networks. Physica A: Statistical Mechanics and its Applications. 2007;375:365–373. [Google Scholar]
- 21.Körner J. Coding of an information source having ambiguous alphabet and the entropy of graphs. 1973. pp. 411–425. Trans 6th Prague Conference on Information Theory.
- 22.Butts C. The complexity of social networks: Theoretical and empirical findings. Social Networks. 2001;23:31–71. [Google Scholar]
- 23.Dehmer M, Mowshowitz A. Inequalities for entropy-based measures of network information content. Applied Mathematics and Computation. 2010;215:4263–4271. [Google Scholar]
- 24.Dehmer M, Mowshowitz A, Emmert-Streib F. Connections between classical and parametric network entropies. PLoS ONE. 2011;6:e15733. doi: 10.1371/journal.pone.0015733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Bonchev D, Trinajstic N. Information theory, distance matrix, and molecular branching. The Journal of Chemical Physics. 1977;67:4517–4533. [Google Scholar]
- 26.Dehmer M, Borgert S, Emmert-Streib F. Entropy bounds for hierarchical molecular networks. PLoS ONE. 2008;3:e3079. doi: 10.1371/journal.pone.0003079. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Skorobogatov VA, Dobrynin AA. Metrical analysis of graphs. MATCH Commun Math Comp Chem. 1988;23:105–155. [Google Scholar]
- 28.Shannon C, Weaver W. The Mathematical Theory of Communication. 1997. University of Illinois Press, Urbana, IL, USA. [PubMed]
- 29.Freeman LC. A set of measures of centrality based on betweenness. Sociometry. 1977;40:35–41. [Google Scholar]
- 30.Freeman LC. Centrality in social networks conceptual clarification. Social Networks. 1978;1:215–239. [Google Scholar]
- 31.Sabidussi G. The centrality index of a graph. Psychometrika. 1966;31:581–603. doi: 10.1007/BF02289527. [DOI] [PubMed] [Google Scholar]
- 32.Emmert-Streib F, Dehmer M. Networks for systems biology: Conceptual connection of data and function. IET Systems Biology. 2011;5:185–207. doi: 10.1049/iet-syb.2010.0025. [DOI] [PubMed] [Google Scholar]
- 33.Simonyi G. Cook W, Lovász L, Seymour P, editors. Graph entropy: A survey. 1995. pp. 399–441. Combinatorial Optimization, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, volume 20.
- 34.Emmert-Streib F, Dehmer M, Kilian J et al HRA, editor. Classification of large graphs by a local tree decomposition. 2006. pp. 477–482. Proceedings of DMIN'05, International Conference on Data Mining, Las Vegas, USA.