This chapter covers
- Manually extracting node features
- Presenting network motifs and graphlets
- Introducing Betweenness and Closeness centralities
- Constructing a monopartite network based on pairwise cosine similarities
- Using community detection algorithm to complete a user segmentation task
This chapter will describe constructing a similarity network based on node properties or features. Similar to typical machine learning preprocessing workflow, each data point or node can be represented as a vector. In the context of graphs, there are generally two approaches you could take to describe a node as a vector. You could manually produce a set of features that describes a node or use node embedding models to produce vectors representing a node in the network automatically. In this chapter, you will manually create representations of nodes to describe their roles in the network and then use those representations to construct an inferred similarity network.
