DISTRIBUTION OF MAXIMAL CLIQUE SIZE UNDER THE WATTS-STROGATZ MODEL OF EVOLUTION OF COMPLEX NETWORKS

International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.3, May 2015
DOI:10.5121/ijfcst.2015.5301 1
DISTRIBUTION OF MAXIMAL CLIQUE SIZE UNDER
THE WATTS-STROGATZ MODEL OF EVOLUTION OF
COMPLEX NETWORKS
Natarajan Meghanathan
Jackson State University, 1400 Lynch St, Jackson, MS, USA
ABSTRACT
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly.
KEYWORDS
Maximal Clique Size, Small-World Networks, Complex Networks, Random Networks, Link Rewiring,
Poisson Distribution, Network Evolution, Watts-Strogatz Model.
1. INTRODUCTION
Network Science is the field of analyzing complex real-world networks from a graph theoretical
standpoint. A complex network is abstracted as a graph wherein the nodes and links in the
network are modeled respectively as the vertices and edges of the graph. The graph theoretic
algorithms run on such complex network graphs (to determine one or more metrics characteristic
of the networks) need to be as efficient as possible. The graph theoretic metrics considered for
analysis of such complex networks [1] include centrality, clustering coefficient, diameter, clique
size, etc. Unlike the polynomial-time efficient algorithms that exist to determine most of the
above metrics, the maximum clique size problem is NP-hard [2]. A "clique" is a complete sub
graph of a graph such that any two vertices in the sub graph are connected with an edge.
Community detection algorithms (e.g., [3-5]) use cliques of various sizes as the basis to determine
closely-knit and overlapping communities in complex real-world networks as well as in networks
that are simulated using theoretical models.
For an n-vertex graph, the "maximum size clique" is the clique of the largest size k such that the
graph does not have any clique of size k+1 (where k ≤ n). Note that there may exist one or more
cliques of smaller size in the graph and for a particular vertex i, the largest size clique it is part of
need not be the maximum size clique for the entire network graph. Accordingly, we define the

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"maximal size clique for a vertex i" as the largest size clique the vertex is part of. The focus of
research in this area has been so far on developing efficient exact algorithms and heuristics (time
and space-wise) to determine respectively the maximum size cliques and approximations to the
same. Not much work has been conducted on determining the maximal size cliques for the
individual vertices of the graph. Specifically, we could not come across any work that has
analyzed the distribution of the maximal clique size for the individual vertices of graphs that
simulate the evolution of complex networks. In this pursuit, we choose a recently proposed
branch-and-bound strategy based efficient exact algorithm [6] to determine maximum size clique
for an entire network graph and modify it to determine the maximal size cliques for the
constituent vertices of the graph. We use the modified exact algorithm to analyze the distribution
of the maximal clique size of the network graphs that span two categories of complex networks:
small-world networks and random networks. We choose the well-known Watts-Strogatz model to
simulate the evolution of small-world networks (from a regular network) and their subsequent
transformation to a random network.
The rest of the paper is organized as follows: In Section 2, we first review and discuss the
recently proposed exact algorithm to determine maximum clique size for an entire graph and then
explain our modifications to the same to determine the maximal clique for a particular vertex of
the graph. Section 3 presents the evolution of small-world networks and their transformation to a
random network under the Watts-Strogatz model and describes the results of the diameter and
clustering coefficient of the networks that evolve during this transformation. Section 4 presents in
detail the distribution of the maximal clique size per node and the average maximal clique size as
a function of the probability of link rewiring and the initial number of links per node. Section 5
reviews the literature and discusses related work. Section 6 concludes the paper. In the entire
paper, the terms 'vertex' and 'node', 'edge' and 'link' have been used interchangeably. They mean
the same.
2. CLIQUE
A clique is a subset of the vertices of a graph such that any two vertices in the subset are
connected with an edge. The maximal clique size and maximum clique size problems are both
NP-hard [2]. There are several exact algorithms (that take exponential run-time at the worst case)
have been proposed to determine the maximum clique size for sparse graphs. With the surge of
research interests in the analysis of complex real-world networks from a graph theoretic
standpoint, we come across efficient algorithms (e.g., [6-9]) to extract the largest size cliques in
large/dense graphs. The "branch-and-bound" strategy is a commonly used strategy [10] behind
these algorithms and the idea is to explore the neighborhood of only those vertices that have
scope for being part of a clique that can be larger than the clique known until then during the
execution of the algorithm. The variation among the various exact algorithms is the pruning
strategy adopted to decide on the bounds for branching through the solution space and limiting
the search. In this section, we will first review a recently proposed exact branch-and-bound based
exact algorithm to determine maximum size clique for an entire network graph and then explain
our modifications to it to determine the maximal size cliques for the individual vertices of a
graph.
The pseudo code of the exact algorithm (proposed in [6]) to determine the maximum size clique
for a graph is outlined in Figure 1. The idea behind the algorithm is to keep track of the maximum
size clique found so far (variable max in the pseudo code, the initial value is 0) and update it
whenever an even larger clique is found. The algorithm proceeds in iterations, with iteration i
attempting to determine whether vertex vi could be part of a clique that is larger than the
maximum size clique known until then. The algorithm decides to explore the neighborhood of a
vertex only if the degree of the vertex is larger than the currently known maximum size clique;

3
moreover only the neighbors whose degree is at least the size of the currently known maximum
clique size are considered for further exploration (these neighbors form the set U passed to the
subroutine CLIQUE). The vertices are explored in the increasing order of their IDs (one iteration
per vertex).
Figure 1. Exact Algorithm to Extract the Maximum Size Clique in a Graph (adapted from [6])
The sub routine CLIQUE (called for a particular vertex vi and its set of neighbors U whose degree
is at least the size of the maximum clique, max, found so far) runs via a combination of iterations
and recursions. In each iteration, the sub routine randomly removes a node u in the set U, adds it
to the clique found so far and recursively calls the sub routine CLIQUE (with vertex u and the set
U comprising of vertices that are the neighbors of u as well as the neighbors of the vertices that
are part of the clique found so far). If the set U passed to the CLIQUE sub routine is empty, the
value of max is updated if it is less than the size of the clique found so far and the recursion is
terminated; otherwise, another recursive call to the sub routine CLIQUE is made with a new
vertex u randomly chosen/removed from the set U of vertices passed during the latest recursion.
The sub routine CLIQUE (called with vertex vi) proceeds in iterations until all the candidate
neighbor vertices in the set U (that was initially passed to it) are explored through a sequence of
recursive calls as explained above. At each stage, the sub routine CLIQUE (called for a particular
vertex vi) only explores those neighbors of vi and their individual neighborhoods that have scope
for being part of a clique that is larger than the maximum size clique found so far (whose size is
kept track of using the variable max).
A characteristic property of the exact algorithm described here is that the maximum size clique
for the entire graph is found during a particular iteration involving vertex vi such that vertices vj
whose neighborhood are explored after this iteration are not part of the clique. If the maximum
size clique for the entire graph is found in an earlier iteration itself, then the subsequent iterations
could run relative more quickly as all they will do is to simply prune the search space as much as
possible. Hence, the time efficiency of the exact algorithm is significantly influenced by the order
in which the vertices are considered to be explored for the iterations as explained in the previous
paragraphs and illustrated in Figure 1. Thus, if vertices are to be explored in the increasing order
of their IDs, the search time could significantly reduce only if a vertex with a smaller ID is part of
the maximum size clique. For guaranteed better performance, it would be more apt to run
MAXCLIQUE by exploring vertices in the decreasing order of their degree, rather than simply
based on the increasing order of their IDs (since the maximum clique size in a graph cannot be
larger than the largest value for the degree of any vertex in the graph).

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The exact algorithm (describe above to determine the maximum size clique for an entire graph)
can be easily modified to determine the maximal size clique for an individual vertex of the graph,
as illustrated in Figure 2. In order to determine the maximal clique size distribution of the vertices
of a graph, we have to now run the procedure MAXIMALCLIQUE for each vertex vi by
considering all the neighbors of vi for possible inclusion in the clique. This would increase the
overall run time of the algorithm; but, it is unavoidable. However, the strategies incorporated by
the exact algorithm in the procedure CLIQUE could be retained: the recursive calls in CLIQUE
are made only for those neighbors u that have the potential to be part of a clique that is larger than
the maximal size clique found until then for vertex vi. For guaranteed better performance of
procedure CLIQUE called with a vertex vi, it would be more apt to explore the neighbors of vi in
the decreasing order of their degree.
Figure 2. Exact Algorithm to Determine the Distribution of the Maximal Clique Size of the Vertices in a
Graph (adapted from [6])
3. SMALL-WORLD NETWORKS AND THEIR CHARACTERISTICS
Small-world networks are a category of complex networks that exhibit a smaller diameter
(maximum of the number of hops in the shortest paths connecting any two nodes) as well as a
larger clustering coefficient (the probability that two nodes that share a common neighbor are
connected). The other two categories of complex networks (random networks and scale-free
networks) exhibit a significantly lower clustering coefficient. Small-world networks are
characteristic of having a majority of the links in the local neighborhood of the vertices (helps to
sustain a larger clustering coefficient) and having very few links (but not negligible) that connect
vertices that do not have any common neighbors (still sufficient enough to ensure a smaller
diameter). As the clustering coefficient of a vertex is measured as the ratio of the number of links
among the neighbors of a vertex to that of the maximum possible number of links among the
neighbors of a vertex, the characteristic of possessing a majority of the links in the local

5
neighborhood of the vertices helps the small-world networks to incur a significantly larger
clustering coefficient, compared to the random networks and scale-free networks.
Initially, we start with a regular network wherein the number of neighbors per node (i.e., the
number of links per node, identified as Kregular) is the same (and typically, an even number of links
per node) as well as there is a particular pattern in the distribution of the links in the network
(typically dependent on the dimension of the network). The regular network envisioned in this
paper is a one-dimensional network with a ring as the underlying topological structure. Each node
is connected to at least two other nodes (i.e., to the two neighboring nodes that are each one hop
away in the ring): if there are more than 2 links per node, then the node is connected to nodes in
the increasing order of the hop count in the ring. In general, if the number of links per node is
Kregular, then a node is connected to neighbor nodes that are 1, 2, ..., Kregular/2 hops away from the
node on the ring. Figure 3 displays a 10-node regular network with four links per node (i.e.,
Kregular = 4) and each node is connected to nodes that are 1 and 2 hops away from it in the ring.
Figure 3. Example for an One-Dimensional Regular Network (Kregular = 4 Links per Node)
The WS model operates based on a tuning parameter called the probability of link rewiring
(Prewire). We rewire each link in the regular network with the probability Prewire. For each link u-v
in the regular network, we generate a random number (in the range 0 to 1) and if it is less than
Prewire, we decide to rewire the link. When a link u-v is chosen for rewiring, we choose a target
node w uniform-randomly among the nodes in the network (such that w is neither u nor v),
remove the link u-v and connect node u with node w (i.e., add the link u-w to the network). We
repeat the above procedure for every link in the initial regular network. Note that the newly added
links are not considered for rewiring.
We conduct simulations to transform a regular network to a small-world network and
subsequently to a random network according to the WS model. The simulations are conducted for
networks of 100 nodes and 200 nodes; the probability of rewiring is varied from values of 0.01 to
0.1, in increments of 0.01 (referred to as small-world network zone), and from values of 0.1 to
1.0, in increments of 0.1 (referred to as random network zone). The reasoning behind the above
distinction for the probability of rewiring is based on our observations from the simulation
results: for Prewire values of 0.01 to 0.1, the average diameter of any node in the network (average
of the maximum of the number of hops from a node to any other node) reduces significantly, but
with only a moderate reduction in the clustering coefficient - a phenomenon characteristic of
small-world networks. On the other hand, as we vary the probability of rewiring from 0.1 to 1.0,
the average diameter of any node in the network reduces only marginally, whereas the clustering
coefficient reduces significantly, indicating the transformation of the small-world network to a
random network. We also vary the initial number of links per node (Kregular) in the regular
network from 4 to 20, in increments of 2. The results presented in Figures 4-10 are the average of

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the results observed for 100 network graphs, simulated for each value of the number of nodes
(100 and 200 nodes) and each value of the probability of rewiring as mentioned above.
Figure 4. Impact of the Probability of Link Rewiring and the Initial Number of Links per Node on the
Average Diameter per Node: Transition from Regular Network to Small-World Network and Random
Network
Figure 4 captures the absolute values of the average diameter of any node in the network as well
as the ratio of the average diameter with and without rewiring. For a given probability of
rewiring, we observe the absolute average value for the diameter to be smaller when we start with
a regular network with a larger number of links per node. As we do rewiring, within the small-
world zone, we observe the differences in the average diameter per node (for different values of
Kregular) to reduce significantly (in an exponential fashion); in the random network zone, the
average diameter per node for different values of Kregular does not vary appreciably. Based on the
results for the ratio of the average diameter per node with and without rewiring, we observe that
the percentage decrease in the average diameter per node is much higher for regular networks
with fewer numbers of initial links, indicating the effectiveness of rewiring in reducing the path
length. With increase in the number of nodes (from 100 nodes to 200 nodes), we observe the
network diameter to reduce further (for a given Prewire and Kregular), especially in the random
network zone.
Figure 5 illustrates the variation in the average clustering coefficient per node (averaged over all
the nodes in the network) in the small-world and random network zones. In the small-world zone,
we observe the percentage reduction in the clustering coefficient is by about only 25% (compared
to the value observed for the originating regular network, without any rewiring) and the rate of
decrease is the same for all values of Kregular. On the other hand, as we enter the random network
zone, the reduction in the clustering coefficient is significantly larger and specifically, the
clustering coefficient of networks that started with a lower Kregular value reduces much faster

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compared to networks that started with a larger Kregular value. As the number of nodes is increased
from 100 to 200, we observe the reduction in the clustering coefficient in the random network
zone (in terms of both the magnitude as well as the rate of decrease) to be larger. For a given
value of Kregular, the fraction of the number of links between any two neighbors of a node
(compared to the maximum number of links between the neighbors of a node) is bound to be
lower for networks with a relatively larger number of nodes, thus reducing the clustering
coefficient.
Average Clustering Coefficient per Node: Transition from Regular Network to Small-World Network and
Random Network
4. ANALYSIS OF THE DISTRIBUTION OF MAXIMAL CLIQUE SIZE
Figure 6 captures the variation in the average maximal clique size per node (average of the
maximal clique size of all the nodes, measured at the end of rewiring) for various values of the
probability of rewiring and the initial number of links per node in the originating regular network.
We observe that the small-world zone does not suffer any noticeable decrease in the average
maximal clique size per node and the ratio of the average maximal clique size per node with and
without rewiring is close to 1. As we transition from the small-world zone to the random network
zone, we observe the average maximal clique size to reduce relatively at a much faster rate, with
increase in the probability of rewiring. An interesting observation is that the average maximal
clique size of random networks that start with a larger Kregular value decreases at a much faster rate
compared to the rate of decrease of the average maximal clique size of random networks that start
with a lower Kregular value (though the absolute values for the average maximal clique size is still
larger for random networks that start with a larger Kregular value). This indicates that the larger
cliques present in the small-world networks that started with regular networks of larger Kregular
values are more likely to quickly get dismantled as the probability of link rewiring increases

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beyond the threshold value of Prewire for small-world network zone. Thus, the average maximal
clique size per nodes for different values of Kregular tend to get closer as we increase the
probability of rewiring in the random network zone, and such a convergence is relatively more
pronounced for networks with 200 nodes, compared to 100 nodes. Accordingly, for a given Prewire
and Kregular, the rate of decrease in the average maximal clique size per node is much more steeper
for networks with 200 nodes.
Average Maximal Clique Size per Node: Transition from Regular Network to Small-World Network and
Random Network
Figures 7-10 capture the variation in the maximal clique size for the nodes in the small-world
zones and random network zones. For a given value of Prewire and Kregular, we observe the
distribution of the maximal clique size is Poisson for both the zones. Figures 7 and 9 capture the
distribution of the maximal clique size in the small-world zone. For smaller values of Kregular (4
and 6 links per node), we observe the maximal clique size per node to be very close to the
average value for all the nodes; as we increase the value of Kregular, we observe the maximal clique
size per node to vary slightly, but not much different from the average value for the maximal
clique size - coinciding with the invariant nature of the average maximal clique size per node
observed in Figure 6. For a given value of Kregular, the average maximal clique size of a regular
network is 1+ Kregular/2 and the average maximal clique size per node in a small-world network is
very close to this value (with minimal variation) observed for its predecessor regular network. For
networks with larger Kregular values, the values for the maximal clique size per node is less than
the average value by at most 2 and greater than the average value by at most 1, and as observed in
Figures 7 and 9, these deviations occur with a vary small probability. The Poisson curve for the
maximal clique size per node shifts to the right in such a way that the peak for the curve increases
by a value of 1 as we increase the value of Kregular by 2. For a given value of Kregular, the Poisson
curve for the maximal clique size per node is more steep (minimal variation) and the tallest for a
Prewire value of 0.01 and becomes more spread out (shallow and more variations) as the Prewire

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value increases to 0.10 and above. Coinciding with the observations made in Figure 6, for a given
value of Kregular and Prewire, there is not much variation in the distribution of the maximal clique
size per node for networks of 100 nodes and 200 nodes.
Figure 7. Distribution of the Maximal Clique Size vs. Initial Number of Links per Node: Transition from
Regular Network to Small World Network [100 Node Network]
Figure 8. Distribution of the Maximal Clique Size vs. Probability of Link Rewiring: Transition from Small
World Network to Random Network [100 Node Network]
Figures 8 and 10 capture the variation in the maximal clique size for the nodes in the random
network zone for a given probability of rewiring and varying the initial number of links per node
with values of 4, 12 and 20 links - scenarios that exhibit minimal, moderate and maximum
variation in the maximal clique size per node as the probability of rewiring increases. For lower
values of the probability of rewiring (0.1 and 0.2; when the network is still in the small-world
zone), the distribution of the maximal clique size per node is taller for each value of Kregular and
the distributions are non overlapping (as the Kregular values are 4, 12 and 20, the average maximal

10
clique size is around 3, 7 and 11 - vindicating the non-overlapping nature of the peaks and the
distribution of the maximal clique size for lower values of Prewire). With increase in Prewire, we start
observing the distributions of the maximal clique size for the three fairly different values of
Kregular to start overlapping; the distributions tend to shift to the left - coinciding with a decrease in
the average maximal clique value. With increase in Prewire, the shift towards lower values of the
maximal clique is more pronounced for networks with a larger Kregular value, vindicating the rapid
fall in the average maximal clique size; also for larger values of Kregular, the distributions for the
maximal clique become more spread out with increase in Prewire - lowering the probability of
finding the maximal clique size per node to be close to the average value. On the other hand, for
networks with lower values of Kregular, the distribution for the maximal clique size remains fairly
narrow (even with increase in Prewire), indicating that it is still possible to observe the maximal
clique size for any node to be close to the average value.
Figure 9. Distribution of the Maximal Clique Size vs. Initial Number of Links per Node: Transition from
Regular Network to Small World Network [200 Node Network]
Figure 10. Distribution of the Maximal Clique Size vs. Probability of Link Rewiring: Transition from Small
World Network to Random Network [200 Node Network]

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5. RELATED WORK
The focus of research in the literature so far has been on developing heuristics (both time and
space efficient) for determining the maximum size for the entire network graphs, based on
strategies (like branch and bound) that reduce the solution search space. For example, branch and
bound strategies based on node degree [6], vertex coloring [7] and vertex ordering [8] have been
proposed as potential strategies for effectively and efficiently pruning the solution search space.
In addition, there have been efforts to develop parallelized versions of branch and bound (e.g.,
[9]) that could be applied to determine cliques in large real-world network graphs with 1000 to
100 million nodes. In [12], the authors explore the use of maximal cliques of size at least k nodes
to identify overlapping communities based on percolation; two cliques of size k are said to
percolate into each other if they share k-1 nodes. With regards to analyzing the distribution of
clique-related metrics for complex networks, in [13], the authors have analyzed the distribution of
clique-degree of the nodes (the clique-degree of a node is the number of cliques of a particular
size the node is part of) in various real-world networks and have observed them to exhibit a
power-law distribution. As far as we know, there is no work that has analyzed the distribution of
the maximal clique size of the vertices for complex network graphs that evolve under any well-
known theoretical model (like the Watts-Strogatz model for small-world networks that eventually
transform to a random network with increase in the probability of link rewiring).
6. CONCLUSIONS
The following significant conclusions could be made from the research conducted in this paper.
with regards to the distribution for the maximal clique size per node for small-world networks and
random networks that evolve from a regular network. As we transform from a regular network
(with Kregular number of links per node) to a small-world network through link rewiring, the
maximal clique size of the nodes is invariant and very close to that of the average maximal clique
size per node as well as close to that of the average maximal clique size per node in the regular
network. As we transform from a small-world network to a random network (by increasing the
probability of rewiring), the distribution of the maximal clique size per node becomes more
broader and thereby the probability of observing a maximal clique size per node close to that of
the average maximal clique size is relatively much lower. Also, with increase in the probability of
rewiring, the distributions for the maximal clique size obtained for different Kregular values overlap
each other and shift towards a lower average value. Nevertheless, for all the scenarios/values for
the probability of rewiring and the initial number of links per node, the distribution for the
maximal clique size reflects that of a Poisson distribution. As the vertices in a random network
exhibit a Poisson style distribution for the node degree, we conjecture a high positive correlation
between the maximal clique size per node and node degree in small-world networks and random
networks, and this will be further analyzed in future work.
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Author
Dr. Natarajan Meghanathan is currently an Associate Professor of Computer Science at
Jackson State University, USA. His areas of research interests are Network Science and
Graph Theory, Wireless Ad hoc Networks and Sensor Networks, Cyber Security and
Machine Learning. He has published more than 150 peer-reviewed articles and obtained
grants from several federal agencies. He serves as the editor-in-chief of three
international journals as well as serves in the organizing committees of several
international conferences.

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