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Prim’s algorithm generates a minimum spanning tree from a graph with weighted edges. Starting in the bottom-left corner, the algorithm keeps a heap of the possible directions the maze could be extended (shown in pink). At each step, the maze is extended in the direction with the lowest weight, as long as doing so does not reconnect with another part of the maze. Here the edges are initialized with random weights.

Unlike Wilson’s algorithm, this does not result in a uniform spanning tree. Sometimes, random traversal is misleadingly referred to as randomized Prim’s algorithm; however, the two algorithms exhibit radically different behavior! The global structure of the maze can be more easily seen by flooding it with color.

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MATCH (a), (b)
WHERE id(a) = 10484 AND id(b) = 10546
MATCH p=shortestPath((a)-[*]-(b))
RETURN p

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= Craft Beers Are Everywhere in Portland =
:neo4j-version: 2.1.2
:author: Kenny Bastani
:twitter: @kennybastani

This GraphGist tells a story about why relationships matter and how craft beers seem to be everywhere in Portland.

== Like Graphs, Craft Beers Are Everywhere

Let me first start out by stating that I (http://www.twitter.com/kennybastani[@kennybastani]) am a happy evangelist for the http://www.neo4j.com[Neo4j Graph Database].

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== This is a test

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= Neo4j GraphDays   =
:neo4j-version: 2.0.1
:author: Kenny Bastani
:twitter: @kennybastani

== Create sample dataset
//setup
//hide
[source,cypher]
----

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-s /Library/Internet\ Plug-Ins/JavaAppletPlugin.plugin/Contents/Home/bin/java /usr/bin

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Unlike choropleth maps, cartograms encode data using area rather than color, resulting in distorted geographic boundaries. In this example, states are rescaled around their centroid, preserving local shape but not topology. Inspired by Zachary Johnson. Non-continguous cartogram design invented by Judy Olsen. U.S. state and county boundaries from the U.S. Census Bureau, simplified using GDAL and MapShaper.

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/*
 * Cypher query:
 * MATCH p=(michael:Person { name: "Michael" })-[r:LIKES]->(people)
 * WHERE r.weight >= 5
 * RETURN p
 */

Transaction tx = db.beginTx();

Node michael = db.findNodesByLabelAndProperty(DynamicLabel.label("Person"), "name", "Michael").iterator().next();

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/**
 * Copyright (C) 2014 Kenny Bastani
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 * in compliance with the License. You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software distributed under the License
 * is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express

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= Entitlements and Access Control Management
:neo4j-version: 2.2.0
:author: Kenny Bastani
:twitter: @kennybastani
:description: Graph database access control, entitlements, authorization solutions
:tags: domain:finance, use-case:access-control

This interactive Neo4j graph tutorial covers entitlements and access control scenarios.

'''