I am pleased to have your help from ![my questions][A lot of unexpected spaces between paragraph after inserting colorbox such as ![this answer][https://tex.stackexchange.com/a/219787/14409] or ![the other answer][https://tex.stackexchange.com/a/219569/14409]. However, there has been another problem which I guess it is the side effect of adding tcolorbox, enumerate, wrapfig together. Below is MWE.

%% https://tex.stackexchange.com/a/126741

skipabove=0pt ,



\section{Complex networks}

\subsection{Small-world networks}
\index{Complex Network!Small-world network}
For many real world phenomena, the average path length $l$ of a network is much 
smaller than that network size $n$, that is $l \ll n$. Such networks are said 
to be characterising the small-world property [1,2]. In 
mathematics, physics and sociology a small-world network (SWN) is a category of 
networks in which most nodes are not neighbours of one another, but most nodes 
can be reached from every other by a small number of \textit{hops} or 
\textit{steps}. D. Watts and S. Strogatz introduced this terminology in 1998 
[5] (also called WS model) that was originated from the famous 
experiment made by Milgram in 1967 [3]. Milgram found that two 
US citizens chosen randomly were connected by an average of six acquaintances.

\subsubsection*{\ding{228} Small-world networks in real life}
small-world networks can be found in many real-world applications, including 
road maps, food chains, electric power grids, metabolite processing networks, 
networks of brain neurons, voter networks, telephone call graphs, and social 
influence networks. These systems comprise of many local links and fewer long 
range \textit{``shortcuts''}, often use with a high degree of local clustering 
relatively small diameter (see more detail below). Networks found in many 
biological and man-made systems are ``small-world networks'', which are highly 
clustered, but the minimum distance between any two randomly chosen nodes in 
graph is short. Thus, studies on SWNs have been interested by many 
researchers in a variety of fields such as mathematics, computer sciences, 
physics, social sciences, etc.

\paragraph{\ding{51}}In a study of Indian physicians [10], they 
have analysed and showed the structure of the Indian railway network (IRN). 
Identifying the stations as nodes of the network and a train which stops at any 
two stations as the edges between the nodes, Sen and co-authors measured the 
average distance between an arbitrary pair of stations and find that it 
depends logarithmically on the total number of stations in the country. While 
from the network point of view this implies the small-world nature of the 
network, in practice a traveller has to change only a few trains to reach an 
arbitrary destination. This implies that over the years, the railway network 
evolved with the sole aim of becoming fast and economical; eventually its 
structure has become a SWN.

 In Goyal's study [6], the principal conditions that a network 
$G$ exhibits \textit{small-world} properties are as the following:
\begin{enumerate}\itemsep1pt \parskip0pt \parsep0pt
\item The number of nodes is very large as compared to the average number 
of links (the average degree), i.e. $n \gg k$
\item The network is integrated; a giant component exists and covers a large 
share of the population.
\item The average distance between nodes $l$ (called characteristic path 
length) in the giant component is small, i.e. $l$ is of order $ln(n)$.
\item The global clustering coefficient is high, i.e. $C \gg k/n$ 
\subsubsection*{\ding{228} Properties of small-world networks}
Based on the definition of SWN proposed by 
[1] and its extensions such as [1], we have described some 
commonly used properties 
of small-work networks as follows:
 \item the network has SCC.
 \item the local neighbourhood is preserved (as for regular lattices).
 \item the diameter of the network increases logarithmically with the number of 
vertices $n$ (as for random networks).
 \item the clustering coefficients are much larger than those of the random 
 \item The average length between two points characterising
global properties of the network was found to depend
strongly on the amount of disorder in the network.
\paragraph{\ding{51}}Another investigation on Boston subway, Latora and his
collaborators [2] showed that the whole transportation system
MBTA\footnote{Boston underground transportation system} (consists of $n = 124$
stations and $k = 124$ tunnels) and bus turns out to be a small-world with a
slight increase in the cost. This paper showed that a generic closed
transportation system can exhibit the small-world behaviour, substantiating 
idea that, in the grand picture, the diffusion of small-world networks can be
interpreted as the need to create networks that are both globally and locally
\textbf{Power-law distribution}~\\
A power law is a
special kind of mathematical relationship between
two quantities. When the number or frequency of an object or event varies as a
power of some attribute of that object (e.g., its size), the number or frequency
is said to follow a power law. For instance, the number of cities having a
certain population size is found to vary as a power of the size of the
population, and hence follows a power 
law \cite{Clauset2009}.
\paragraph{\ding{51}}The World Wide Web has a small-world topology as well 
[12]. In this paper, Adamic made a comparison between SWNs
 for sites, and the corresponding random graphs, the subset of \textit{.edu} 
sites was considered. Because the \textit{.edu} subset is significantly 
 distances between every node could be computed. $3,456$ of the $11,000$
\textit{.edu} sites formed the largest SCC. In
summary, the largest SCCs of both sites in general
and the subset of \textit{.edu} sites are SWNs with small
average minimum distances.

\paragraph{\ding{51}}In fact, rich-species food webs with a good taxonomic 
resolution display the properties of small-world behaviour [1]. 
Montoya and Sol\'e analysed the four large food webs and compared between real 
webs and randomly generated webs. Consequently, they approved that the 
clustering coefficient of both types is the same average number of links per 
species. One important result is that in all cases, the clustering coefficient 
is clearly larger than the one of the random networks. For the characteristic 
path length, the difference between the random and real case is almost very 

\paragraph{}On the other hand, these scale-free networks own the power-law
behaviour means that most vertices are connected
sparsely, while a few vertices are connected intensively to many
others and play an important role in functionality illustrates the difference 
random and scale-free network.
  • 2
    What is the question (i.e. the side effect)??? – user31729 Dec 29 '14 at 0:07
  • 1
    Why do you have \paragraph{}? – cfr Dec 29 '14 at 0:09
  • 1
    You shouldn't try to use an environment based on wrapfig near a list such as an itemize. Putting it in the figure being wrapped is different. But wrapping a list around the figure is not going to work. The documentation is extremely clear on this point: it needs to be used in regular paragraph text i.e. not too near a section heading or anything based on a list. – cfr Dec 29 '14 at 0:17
  • 1
    @Tung: cfr is completely right: The wrapfig manual states clearly on the first page that lists like itemize etc. and wrapfig don't behave well together – user31729 Dec 29 '14 at 0:23
  • 1
    @Tung: We won't pay for that article (30 Euros .... ridiculous!) and I am sure, they applied some other technique to achieve this – user31729 Dec 29 '14 at 0:32

When you use any environment based on wrapfig you must provide sufficient regular-paragraph text or you will get extremely odd results. The documentation makes this extremely clear: it is a known, well-advertised limitation of the package.

The kinds of things which will cause problems include using a wrapfig environment near:

  • a section heading;
  • any environment based on the list environment, including itemize, enumerate, quotation, quote etc.

Putting a list in the figure being wrapped is fine. But wrapping a list around the figure is not going to work.

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