Generating Graphs
Generate undirected (random direction) graphs with semi-real random distributions based on theoretical models. Providing
a node label will generate nodes with that label and a random uuid property. Providing a relationship type will generate
relationships with that type. Providing null in place of labels and/or relationships will generate random Person nodes
with a random name and FRIEND_OF relationships, respectively.
For a theoretical introduction, please take a look at the following blog posts:
For permitted configuration values, please look at the Javadoc of config classes of the respective generators (BarabasiAlbertConfig,
ErdosRenyiConfig, WattsStrogatzConfig, and DistributionBasedConfig).
Apart from the number of nodes you would like to generate, null can be passed in for all other parameters. Sensible
defaults will be used.
| type | qualified name | signature | description |
|---|---|---|---|
procedure |
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apoc.generate.ba(noNodes, edgesPerNode, label, type) - generates a random graph according to the Barabasi-Albert model |
procedure |
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apoc.generate.complete(noNodes, label, type) - generates a random complete graph |
procedure |
|
|
apoc.generate.er(noNodes, noEdges, label, type) - generates a random graph according to the Erdos-Renyi model |
procedure |
|
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apoc.generate.simple(degrees, label, type) - generates a simple random graph according to the given degree distribution |
procedure |
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apoc.generate.ws(noNodes, degree, beta, label, type) - generates a random graph according to the Watts-Strogatz model |
Example
CALL apoc.generate.ba(1000, 2, 'TestLabel', 'TEST_REL_TYPE')
CALL apoc.generate.ws(1000, null, null, null)
CALL apoc.generate.simple([2,2,2,2], null, null)