One of the best things about working with graphs is the flexibility of the graph model.
As part of a tennis predictions app that I've been building, I needed to come up with a way to represent the actual result of a match alongside the predictions done by participants.
After trying a few approaches, I eventually settled on what I like to think of as a shadow graph. We keep the initial subgraph representing each match, but then attach a match prediction subgraph to that match for each player, with precisely the same structure.
This technique makes it easy to add new predictions to the graph, as well as query the graph to compute prediction accuracy.
In this talk, I'll show the graph model before doing a quick walk-through of the model in action based on my (not very good!) predictions of tennis tournaments in 2022.
Speakers: Mark Needham
Format: Lighting Talk 10-15 min
Level: Beginner
Topics: #General, #Beginner
Region: AMERICAS
Slides: https://dist.neo4j.com/nodes-20202-slides/012%20Graph%20Modeling%20The%20Shadow%20Graph%20-%20NODES2022%20AMERICAS%20Beginner%205%20-%20Mark%20Needham.pdf
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