IEEE Circuits and Systems Magazine - Q4 2019 - 52

the scale-free behavior for three cities, and it was very interesting to observe that the Hong Kong network plausibly
exhibited the scale-free property with the supernode representation, as shown in Fig. 2. In other words, a slight modification in the topological representation permitted the
exposition of an important network property which otherwise was undetected under conventional graph representation. Therefore, the effect of supernodes in analyzing the
public transport networks should not be overlooked.
Finally, it is very interesting to observe the scalefree property (sometimes called the 80/20 rule) in public transport networks. This demonstrates the fact a
myriad number of stops carry 20% of the network load,
and a countable number of stops carry 80% of the load.
Public transport networks having such a property are
free of any scaling applied to them. The mechanism of
passenger flow in a scale-free network is an important
research topic from the perspective of a transport engineer, similar to the study of information spread or
disease spreading by network engineers and biologists.
Another core research area of practical importance is
robustness analysis which aims to study the network
functionality upon removal of a certain set of target

nodes. It has been shown that scale-free networks are
more prone to targeted attacks, in contrary to random
networks which end up merely at network fragmentation on targeted attacks.
C. Network Cohesiveness
The extent to which the immediate neighbors of a node
are connected to each other is examined through a property called clustering, which defines the level of cohesiveness
in a network. Clustering, also known as the transitivity,
is a local property dealing with node level information
in network theory. The cohesiveness of nodes is evaluated at local level through a parameter called local clustering coefficient, which is given by

/ a ij a ih a jh
Ci =

/ / (a ij + a ji) (a jh + a hj) (a hi + a ih)
Ci =

2 [k i (k i - 1) - 2k i)]

;
(8)

100
Empirical Value
Estimated Value
10-1
Pr (K ≥ Kmin)

Pr (K ≥ Kmin)

h

i!j

10-2

10-2
10-3

10-3

101
(K)

10-4 0
10

102

101
(K)

102

100

100

Empirical Value
Estimated Value

Empirical Value
Estimated Value

10-1
Pr (K ≥ Kmin)

10-1
Pr (K ≥ Kmin)

j

k i) = / a ij a ji

Empirical Value
Estimated Value

10-1

10-2

10-2
10-3

10-3

10-4 0
10

(7)

k i (k i - 1)

for undirected networks, and

100

10-4 0
10

j, h

101
(K)
(a)

102

10-4 0
10

101
(K)

102

(b)

Figure 2. Power law fit for (a) in-degree distribution; (b) out-degree distribution under regular (upper) and supernode (lower) representations.
52

IEEE CIRCUITS AND SYSTEMS MAGAZINE

FOURTH QUARTER 2019



IEEE Circuits and Systems Magazine - Q4 2019

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