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2017 | Buch

Theoretical Concepts of Network Analysis Kapitel lesen Erstes Kapitel lesen

Python for Graph and Network Analysis

verfasst von: Mohammed Zuhair Al-Taie, Seifedine Kadry

Verlag: Springer International Publishing

Buchreihe : Advanced Information and Knowledge Processing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities.

Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Theoretical Concepts of Network Analysis
Abstract
Generally speaking, a network is a set of links (ties or edges) and objects (nodes or vertices). These objects could be people, rivers, roads, computers, cities, etc., while links may represent relationships such as friendship, kinship, sexual relationships, the flow of information, etc. Kinds of networks include computer networks, neural networks, semantic networks, food web, supply chain networks, friendship networks, information networks, etc. Network representation borrows some of its notations (e.g., nodes and links) from graph theory and other notations (e.g., the actor-network theory) from social theories.
Mohammed Zuhair Al-Taie, Seifedine Kadry
Chapter 2. Network Basics
Abstract
This chapter introduces the concept of a network, which is obviously the core object of network analysis. We will discuss topics such as types of networks, network measures, installation and use of NetworkX library, network data representation, basic matrix operations, and data visualization.
What Is a Network?
A network can be a specialized type of mathematical graph or interconnected systems. Hence, it is not far from a graph, which implies the visual representation of a set of nodes and edges. Network nodes may represent web pages, people, organizations, articles, places, and many other things.
Mohammed Zuhair Al-Taie, Seifedine Kadry
Chapter 3. Graph Theory
Abstract
The chapter introduces the main features of graph theory, the mathematical study of the application, and properties of graphs, initially motivated by the study of games of chance. It addresses topics such as origins of graph theory, graph basics, types of graphs, graph traversals, and types of operations on graphs.
Mohammed Zuhair Al-Taie, Seifedine Kadry
Chapter 4. Social Networks
Abstract
This chapter introduces the main concepts of social networks such as properties of social networks, data collection in social networks, data sampling, and social network analysis.
Mohammed Zuhair Al-Taie, Seifedine Kadry
Chapter 5. Node-Level Analysis
Abstract
This chapter is concerned with building an understanding of how to do network analysis at the node (ego) level. It shows how to create social networks from scratch, how to import networks, how to find key players in social networks using centrality measures, and how to visualize networks. We will also introduce the important algorithms that are used to gain insights from graphs.
Mohammed Zuhair Al-Taie, Seifedine Kadry
Chapter 6. Group-Level Analysis
Abstract
In this chapter, we are going to present a number of techniques for detecting cohesive groups in networks such as cliques, clustering coefficient, triadic analysis, structural holes, brokerage, transitivity, hierarchical clustering, and blockmodels. All of which are based on how nodes in a network interconnect. However, among all, cohesion and brokerage types of analysis are two major research topics in social network analysis.
Mohammed Zuhair Al-Taie, Seifedine Kadry
Chapter 7. Network-Level Analysis
Abstract
In this chapter, we are going to study graphs and networks as a whole, which is different from what we had done in the previous chapters when we analyzed graphs at the node level and the group level. Hence, this chapter addresses concepts such as components and isolates, cores and periphery, network density, shortest paths, reciprocity, affiliation networks and two-mode networks, and homophily.
Mohammed Zuhair Al-Taie, Seifedine Kadry
Chapter 8. Information Diffusion in Social Networks
Abstract
In this chapter, we will discuss concepts of information diffusion in social networks. We are interested in knowing how a piece of information (knowledge) is spread through a network. These may be computer viruses spreading on the Internet or a network of computers, diseases through a social network, or rumors and ideas through a social network. Information diffusion methods are commonly used in viral marketing, in collaborative filtering systems, in emergency management, in community detection, and in the study of citation networks.
Mohammed Zuhair Al-Taie, Seifedine Kadry
Backmatter
Metadaten
Titel
Python for Graph and Network Analysis
verfasst von
Mohammed Zuhair Al-Taie
Seifedine Kadry
Copyright-Jahr
2017
Electronic ISBN
978-3-319-53004-8
Print ISBN
978-3-319-53003-1
DOI
https://doi.org/10.1007/978-3-319-53004-8

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