Neo4j: What is a node?
One of the first things I needed to learn when I started using Neo4j was how to model my domain using nodes and relationships and it wasn’t initially obvious to me what things should be nodes.
Luckily Ian Robinson showed me a mini-algorithm which I found helpful for getting started. The steps are as follows:
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Write out the questions you want to ask
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Highlight/underline the nouns
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Those are your nodes!
This is reasonably similar to the way that we work out what our objects should be when we’re doing OO modelling and I thought I’d give it a try on some of the data sets that I’ve worked with recently:
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Female friends of friends that somebody could go out with
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Goals scored by Arsenal players in a particular season
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Colleagues who have similar skills to me
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Episodes of a TV program that a particular actor appeared in
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Customers who would be affected if a piece of equipment went in for repair
If you’re like me and aren’t that great at English grammar we can always cheat and get NLTK to help us out:
>>> nltk.pos_tag(nltk.word_tokenize("Female friends of friends that somebody could go out with"))
[('Female', 'NNP'), ('friends', 'NNS'), ('of', 'IN'), ('friends', 'NNS'), ('that', 'WDT'), ('somebody', 'NN'), ('could', 'MD'), ('go', 'VB'), ('out', 'RP'), ('with', 'IN')]That tells us the likely tag for each part of speech in the sentence and we can filter the resulting list so we only see nouns like this:
>>> nouns = ['NNS', 'NN', 'NP', 'NNP']
>>> [(word, grammar) for (word, grammar) in nltk.pos_tag(nltk.word_tokenize("Female friends of friends that somebody could go out with")) if grammar in nouns]
[('Female', 'NNP'), ('friends', 'NNS'), ('friends', 'NNS'), ('somebody', 'NN')]We can ignore the 'Female' in this sentence (I think it’s been picked up as a proper noun because of the capitalisation) which leaves us with 'friends' and 'somebody' In both cases these nouns represent the concept of a person so we’d want to create nodes representing people in this domain.
Let’s see how NLTK gets on with our second question:
>>> sentence = "Goals scored by Arsenal players in a particular season"
>>> [(word, grammar) for (word, grammar) in nltk.pos_tag(nltk.word_tokenize(sentence)) if grammar in ['NNS', 'NN', 'NP']]
[('Goals', 'NNS'), ('Arsenal', 'NNP'), ('players', 'NNS'), ('season', 'NN')]In this case we’d have goals, teams (e.g. Arsenal), players and seasons as our nodes.
Although this is a very rough algorithm for working out what things should be nodes in a graph I think it’s a good way to get started.
After that the other queries we want to write may lead us to change the model to solve our problem even better.
About the author
I'm currently working on short form content at ClickHouse. I publish short 5 minute videos showing how to solve data problems on YouTube @LearnDataWithMark. I previously worked on graph analytics at Neo4j, where I also co-authored the O'Reilly Graph Algorithms Book with Amy Hodler.