Graph analysis is all about finding relationships. In this post I show how to compute graph density (a ratio of how well connected relationships in a graph are) using a Cypher query with Neo4j. This is a follow up to the earlier post: SPARQL Query for Graph Density Analysis.
In preparation for a post about doing graph analytics in Neo4j (paralleling SPARQLverse from this earlier post), I had to learn to load text/CSV data into Neo. This post just shows the steps I took to load nodes and then establish edges/relationships in the database.
My head hurt trying to find a simple example of loading the data I had used in my earlier example but this was because I was new to the Cypher language. I was getting really hung up on previewing the data in the Neo4j visualiser and finding that all my nodes had only ID numbers was really confusing me. I had thought it wasn’t loading my name properties or something when it was really just a visualisation setting (more on that another time). Anyway, enough distractions… Continue reading Graph relations in Neo4j – simple load example
“One area where graph analytics particularly earns its stripes is in data discovery. While most of the discussion around big data has centered on how to answer a particular question or achieve a specific outcome, graph analytics enables us, in many cases, to discover the “unknown unknowns” — to see patterns in the data when we don’t know the right question to ask in the first place.”
I’ve been spending a lot of time this past year running queries against the open source SPARQLversegraph analytic engine. It’s amazing how simple some queries can look and yet how much work is being done behind the scenes.
My current project requires building up a set of query examples that allow typical kinds of graph/network analytics – starting with the kinds of queries needed for Social Network Analysis (SNA), i.e. find friends of friends, graph density and more.