In this section, we quickly cover additional information that may be helpful for some readers. We’ll look at other types of algorithms, another way to import data into Neo4j, and another procedure library. There are also some resources for finding datasets, platform assistance, and training.
Many algorithms can be used with graph data. In this book, we’ve focused on those that are most representative of classic graph algorithms and those of most use to application developers. Some algorithms, such as coloring and heuristics, have been omitted because they are either of more interest in academic cases or can be easily derived.
Other algorithms, such as edge-based community detection, are interesting but have yet to be implemented in Neo4j or Apache Spark. We expect the list of graph algorithms used in both platforms to increase as the use of graph analytics grows.
There are also categories of algorithms that are used with graphs but aren’t strictly graphy in nature. For example, we looked at a few algorithms used in the context of machine learning in Chapter 8. Another area of note is similarity algorithms, which are often applied to recommendations and link prediction. Similarity algorithms work out which nodes most resemble each other by using various methods to compare items like node attributes.
Importing data into Neo4j with the Cypher query language uses a transactional approach. ...