5 Introduction to social network analysis

 

This chapter covers

  • Presenting random and scale-free network degree distribution
  • Using metrics to characterize a network
  • Introducing the Neo4j Graph Data Science library
  • Using native projection to project an in-memory graph
  • Inspecting the community structure of a graph
  • Finding influencers in the network with PageRank

5.1 Follower network

5.1.1 Node degree distribution

5.2 Introduction to the Neo4j Graph Data Science library

5.2.1 Graph catalog and native projection

5.3 Network characterization

5.3.1 Weakly connected component algorithm

5.3.2 Strongly connected components algorithm

5.3.3 Local clustering coefficient

5.4 Identifying central nodes

5.4.1 PageRank algorithm

5.4.2 Personalized PageRank algorithm

5.4.3 Dropping the named graph

5.5 Solutions to exercises

Summary