This chapter covers:
- Exploring graph with Cypher query language
- Filtering and aggregating data with Cypher query language
- Introducing Neo4j Graph Data Science library
- Inspecting community structure of a graph
- Defining node roles and finding influencers
This chapter will teach you how to perform a social network analysis of the imported Twitter network. Imagine you are working as a social media consultant and want to find as many insights as possible. As is typical with any analysis, you will begin with exploratory data analysis to get an overview of the data you are working with.
I will present how I collected the data to give you a feeling of the data you will be working on in this chapter, The imported Twitter social network was scraped using the official Twitter API. I have fetched tweets that are part of the NLP or Knowledge graph topics. At this time, I had information about tweets and users who were either mentioned or published tweets. Next, I fetched additional meta-data about users in the graph, such as their registration date and follower relationships. All the users in the imported graph have either published a tweet or were mentioned by one. I did not want to include all the user followers because that would explode the graph, and the network would end up consisting of a couple of million users.