Graphics Reference
In-Depth Information
Note
The analysis of clusters and communities is outlined in Chapter 11,
“Communities,” and the theme is picked up again in Chapter 14, “Big
Data.”
Analysis of social networks can provide insights into clusters of people
or organizations and influential connections within and between those
clusters. These insights can be used to understand diffusion through a
network (such as spread of coupons or a virus) and to understand
communities (such as customer segmentation based on connections).
Graphs Today
In the age of Big Data, many of the world's most data-rich businesses are
searching for new ways to make sense of vast streams of complex, irregular,
sometimes unverifiable, interconnected data. Graph analysis and
visualization is gaining momentum as a tool for helping to do just that.
Graphs are particularly good at characterizing complex, compound
relationships that are not easily described in black-and-white terms. They
are also a natural choice for displaying networks, which are an increasingly
integral part of many business data sets.
Desktop tools like Gephi and Cytoscape (which typically originate in
scientific communities) have made strides in visual quality and scale for
graph visualization and analysis. With their open and extensible nature,
these tools can be easily applied to business problems, given the right
amount of technical training and determination. With the prospect of
cloud-based systems on the horizon, graphs promise to become even more
easily accessible to the wider community of business analysts.
The goal of this topic is to inspire creative thinking about the potential
application of graphs to your own business problems and to share a little
of our own domain knowledge in the hopes that you may try it yourself.
Step-by-step tool usage and code samples are provided using case examples
that demonstrate how graph analysis and visualization can be used to gain
insights from data.
