Posegga, Oliver

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Network Analysis in Python
A Brief Introduction
Oliver Posegga
University of Bamberg,
An der Weberei 5, 96047 Bamberg, Germany
http://www.uni-bamberg.de/sna/
Abstract. This tutorial provides an entry-level introduction to social net-
work analysis using Python and NetworkX. We discuss Python as a
platform for network analysis and provide a systematic overview on the
available libraries that elevate Python to a powerful toolbox for network
research. Further, we introduce the fundamental concepts of network
analysis and visualization, which we illustrate with practical examples
based on a freely available dataset that we analyze with the software in-
troduced in the beginning of the tutorial.
ȟ Goals
The goal of this tutorial is to provide a brief but comprehensive introduction to so-
cial network analysis using Python. After attending the tutorial, participants will
be familiar with the basic concepts of network analysis, know how to analyze net-
works from a multi-level multi-theory perspective, understand the fundamentals
of the Python ecosystem and know how to quickly setup their own Python environ-
ment. Further, they will be able to model, analyze, and visualize simple networks
from freely available datasets using contemporary Python libraries.
Ƞ Requirements
This course is designed to be an entry-level tutorial for individuals interested in
social network analysis and serves as a starting point and overview on the topic.
Participants are not required to be familiar with social network analysis and related
software in general. Previous knowledge, however, will be beneﬁcial to understand
the topics of the tutorial. Participants who are already familiar with social network
SOCNET 2018, February 28, 2018 3
https://doi.org/10.20378/irbo-51793
analysis and Python are not the primary target audience of the tutorial, but might
be interested in some of the advanced topics (e.g. interactive network visualization
using Python and d3.js [2]), which are brieﬂy discussed and demonstrated as an
outlook at the end of the tutorial.
ȡ Structure and Content
The tutorial covers threemajor topics, i.e. (1) the Python ecosystem, including net-
work analysis and visualization libraries, (2) the fundamentals of network analy-
sis, and (3) the fundamentals of network visualization. The contents discussed for
each topic are brieﬂy outlined in the following.
ȡ.ȟ Python and Social Network Analysis
There are multiple readily available software solutions that come with many of
the diﬀerent methods and techniques falling into the domain of social network
analysis, some of which have been around for decades (e.g. UCINET [8], Pajek
[7], Gephi [4]). Recently, however, Python and the ecosystem evolving around it
have gained popularity in the network community. As a programming language,
Python is known for its intuitive syntax, readability, extensibility, versatility, cross-
platform availability, high degree of customizability, and the large community that
has emerged from it. As a platform for social network analysis, it primarily beneﬁts
from the extensive number of libraries and extensions contributed by this commu-
nity, which elevate Python from a simple programming language to a ﬂexible and
powerful ecosystem with a wide range of scientiﬁc applications.
Over the course of the tutorial, we provide a brief overview of the various libraries
that are necessary to use Python for social network analysis. For this tutorial, we
use a freely available Python distribution, i.e. Anaconda [1], which provides a com-
prehensive scientiﬁc Python environment, including Jupyter [5], a web-based, in-
teractive development environment, formerly known as iPython.
We brieﬂy discuss the scientiﬁc Python environment and its setup, before we
demonstrate how Anaconda [1] can be used in conjunction with Docker [3] to pro-
vide a ﬂexible cross-platform environment for network analysis. Further, we pro-
vide an overview of themost commonly used network analysis libraries for Python
and continue the tutorial with a practical introduction to the NetworkX [6] library
using practical examples based on a freely available dataset.
4 International Workshop of MMB 2018
ȡ.Ƞ The Fundamentals of Social Network Analysis
We discuss the basic concepts of network analysis using the previously introduced
dataset based on simple analyses conducted with NetworkX [6]. Over the course of
this introduction, we follow an exploratory analysis pattern [9], which comprises
the following steps: Deﬁnition of nodes and edges, manipulation of the network,
computation of network measures, visualization of the network.
In the deﬁnition step, we discuss the implicit and explicit assumptions that have
to be made when modeling network structures from diﬀerent types of network
data.With regard to themanipulation step, we explain several approaches to query-
ing andmanipulating network structures usingNetworkX [6]. After covering those
steps, we proceed with the introduction of well-known network measures along a
multi-level multi-theory framework [10], which systematically captures diﬀerent
units of analysis, ranging from the level of individual actors to the network level.
Finally, we cover the visualization of networks in general, and in Python in partic-
ular, in a dedicated section of this tutorial.
ȡ.ȡ The Fundamentals of Network Visualization
One of the profound strengths of network analysis lies within the beauty and in-
tuitive nature of network visualizations. While it has become deceptively easy to
visualize networks using tools like Gephi [4], which provide easy access to a variety
of sophisticated layout algorithms and a plethora of useful visualization features,
creating meaningful visualizations requires a systematic understanding of their
building blocks.
We brieﬂy discuss those building blocks and provide an overview of the most
common layout algorithms used in practice. Using the dataset analyzed in the ﬁrst
part of this tutorial, we demonstrate how to create simple network visualizations
using NetworkX [6].
The last part of this tutorial is dedicated to the discussion of the interactive vi-
sualization of networks. We demonstrate two approaches to create such visual-
izations: The ﬁrst approach is based on exporting network data from Python and
importing them into Gephi [4]. The second approach utilizes the d3.js [2] frame-
work in conjunction with Python and NetworkX [6].
SOCNET 2018, February 28, 2018 5
References
1. Anaconda. https://anaconda.org/
2. d3js. https://d3js.org/
3. Docker. https://www.docker.com/
4. Gephi. https://gephi.org/
5. Jupyter. http://jupyter.org/
6. Networkx. https://networkx.github.io/
7. Pajek. http://mrvar.fdv.uni-lj.si/pajek/
8. Ucinet. https://sites.google.com/site/ucinetsoftware/home/
9. De Nooy, W., Mrvar, A., Batagelj, V.: Exploratory social network analysis with Pajek,
vol. 27. Cambridge University Press (2011)
10. Monge, P.R., Contractor, N.S.: Theories of communication networks. Oxford Uni-
versity Press, USA (2003)
6 International Workshop of MMB 2018


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