Neo4j: What is a Node?
Neo4j: What is a Node?
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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:
- Write out the questions you want to ask
- Highlight/underline the nouns
- 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:
- Female friends of friends that somebody could go out with
- Goals scored by Arsenal players in a particular season
- Colleagues who have similar skills to me
- Episodes of a TV program that a particular actor appeared in
- 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.
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