Real-world graphs are not random. Example real-world graphs include social graphs, word graphs, neural graphs, airline graphs, water flow graphs, etc. Interestingly enough, there is a simple statistical understanding of most natural graphs. In short, many vertices have few connections and very few vertices have many connections. A popular algorithm to generate a graph that has this connectivity pattern is known as the preferential attachment algorithm which can be simply described with the colloquial phrase: "the rich get richer." This section provides some simple code to artificially generate a natural looking graph in JanusGraph using Gremlin.
The first thing to do is to connect to a Cassandra cluster with JanusGraph. In the example below, a local connection is used where storage.batch-loading ensures more speedy performance.
conf = new BaseConfiguration()
conf.setProperty('storage.backend', 'cassandra')
conf.setProperty('storage.hostname', '127.0.0.1')
conf.setProperty('storage.batch-loading', 'true')
graph = JanusGraphFactory.open(conf)
g = graph.traversal()Next, the following script generates a graph with size number of edges.
mgmt = graph.openManagement()
follow = mgmt.makeEdgeLabel('linked').multiplicity(MULTI).make()
mgmt.commit()
size = 100000; ids = [g.addVertex().id()]; rand = new Random()
(1..size).each {
v = graph.addVertex()
u = g.V(ids.get(rand.nextInt(ids.size()))).next()
v.addEdge('linked', u)
ids.add(u.id())
ids.add(v.id())
if (it % 1000 == 0)
graph.tx().commit()
}