Book Image

Network Science with Python and NetworkX Quick Start Guide

By : Edward L. Platt
Book Image

Network Science with Python and NetworkX Quick Start Guide

By: Edward L. Platt

Overview of this book

NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use. If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts. By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems.
Table of Contents (15 chapters)

Strong and weak ties

In social networks, not all relationships are created equal. You might cosign a loan application for your sibling, but probably not for your cousin's babysitter's dentist's chimney sweep. In sociology, the strength of a relationship is captured by the concept of tie strength. In this context, a tie is some kind of an interpersonal relationship, and the strength is any measure of how intense or intimate that relationship is.

In 1973, the sociologist Mark Granovetter described the importance of weak ties in bridging different communities. If all ties within a community are strong, then any ties between communities must be weak. He described this phenomenon as the strength of weak ties. By bridging different communities, weak ties make it possible to find information from distant parts of a network. But how do we measure tie strength?

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