Community Detection - Lab

Introduction

In this lab, you'll once again work to cluster a network dataset. This time, you'll be investigating a meetup dataset for events occurring in Nashville.

Objectives

You will be able to:

Cluster a social network into subgroups

Load the Dataset

To start, load the dataset 'nashville-meetup/group-edges.csv' as a pandas Dataframe.

#Your code here

To add some descriptive data, import the file 'nashville-meetup/meta-groups.csv'.

#Your code here

Transform to a Network Representation

Take the Pandas DataFrame and transform it into a graph representation via NetworkX. Make nodes the names of the various groups within the Nashville area. The edges should be the weights between these groups.

#Your code here

Visualize the Network

#Your code here

Refine the Visual

As you should see, the initial visualization is a globular mess! Refine the visualization to better picture the center core of the network.

#Your code here

Try Clustering the Network

Now, that the dataset is a little more manageable, try clustering the remaining group nodes.

#Your code here

Determine An Optimal Clustering Schema

Finally, determine a final clustering organization.

#Your code here

Visualize the Clusters

Analyze the output of your clustering schema. Do any clusters of groups stand out to you?

#Your code here

Summary

In this lab, you got another chance to practice your network clustering skills, investigating a meetup dataset of events and groups surrounding the Nashville area. If you're up for the challenge, feel free to continue investigating the dataset and trying to cluster users or events!

sherzyang/dsc-3-28-12-graph-connectivity-community-detection-seattle-ds-career-040119