我遇到了对象下方与环境tcolorbox内部出现意外间距的问题wrapfigure。您可以查看附加的快照。
以下是我的 MWE。
\documentclass[a4paper,12pt]{article}
\usepackage[scaled]{helvet}
\renewcommand\familydefault{\sfdefault}
\usepackage[utf8]{inputenc}
\usepackage{pifont}
\usepackage{wrapfig}
\usepackage[framemethod=tikz]{mdframed}
\usepackage{xcolor,tcolorbox}
%% https://tex.stackexchange.com/a/126741
\newenvironment{WrapText1}[1][r]
{\wrapfigure{#1}{0.5\textwidth}\tcolorbox}
{\endtcolorbox\endwrapfigure}
\newenvironment{WrapText2}[1][r]
{\wrapfigure{#1}{0.5\textwidth}\mdframed[backgroundcolor=gray!20,skipabove=0pt,
skipbelow=0pt]}
{\endmdframed\endwrapfigure}
%opening
\title{}
\author{}
\begin{document}
\maketitle
\section{Complex networks}
\subsection{Small-world networks}
\label{sec:small-world}
\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
but
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
the
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
railway
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
has
evolved with the sole aim of becoming fast and economical; eventually its
structure has become a SWN.
\begin{WrapText2}
\vspace{-1.0mm}
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. $C \gg k/n$
\end{enumerate}
\vspace{-1.0mm}
\end{WrapText2}
\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
the
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
efficient.
\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
smaller,
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
small.
\end{document}
我参考了在这个答案，甚至两天前我通过提问提出了类似的方法 这个答案。
问题是：如何删除框下方或上方的意外空格？
答案1
环境wrapfig有一个可选的第一个参数，用于表示要使用的行数（然而，这是一些反复试验的方法，以找到“正确”的值）（我现在不记得默认值是什么，但我相信是 12）
我改变了环境的定义\WrapText1，并添加了一个强制的第二个参数来指定为行号。
我做到了不是删除文本等并执行不是在 OP 的 MWE 中寻找其他问题
\documentclass[a4paper,12pt]{article}
\usepackage[scaled]{helvet}
\renewcommand\familydefault{\sfdefault}
\usepackage[utf8]{inputenc}
\usepackage{pifont}
\usepackage{wrapfig}
\usepackage[framemethod=tikz]{mdframed}
\usepackage{xcolor,tcolorbox}
%% http://tex.stackexchange.com/a/126741
\newenvironment{WrapText1}[2][r]
{\wrapfigure[#2]{#1}{0.5\textwidth}\tcolorbox}
{\endtcolorbox\endwrapfigure}
\newenvironment{WrapText2}[2][r]
{\wrapfigure[#2]{#1}{0.5\textwidth}\mdframed[backgroundcolor=gray!20,skipabove=0pt,
skipbelow=0pt]}
{\endmdframed\endwrapfigure}
%opening
\title{}
\author{}
\begin{document}
\maketitle
\section{Complex networks}
\subsection{Small-world networks}
\label{sec:small-world}
\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
but
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
the
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
railway
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
has
evolved with the sole aim of becoming fast and economical; eventually its
structure has become a SWN.
\begin{WrapText2}{20}
% \vspace{-1.0mm}
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. $C \gg k/n$
\end{enumerate}
\vspace{-1.0mm}
\end{WrapText2}
\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
the
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
efficient.
\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
smaller,
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
small.
\end{document}
