William Lyon johnymontana

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Please note there is now a dedicated project for this: https://github.com/jexp/spoon-neo4j/

Adds these features to Neo4j Browser

DataTable (search, sort, paginage)
Zoom for graphs and query plans (Hold Alt- and drag / pan)
Charts (Currently Line-Charts)

##Neo4j GraphGist - Marketing Recommendations Using Last Touch Attribution Modeling and k-NN Binary Cosine Similarity

Neo4j Use Case: Real-Time Marketing Recommendations

#Part 1. Neo4j Marketing Attribution Models

Introduction

Graphs are well suited for marketing analytics - a natural fit since marketing is principally about relationships. In this GraphGist, we'll take a look at how to use Neo4j to make real-time marketing recommendations.

Github Events Analysis with Neo4j

On July 22, Github announced the 3rd Annual Github Data Challenge presenting multiple sources of data available.

This sounded to me a good opportunity to use their available data and import it in Neo4j in order to have a lot of fun at analyzing the data that fits naturally in a graph.

As I work mainly offline or behind military proxies that do not permit me to use the ReST API, I decided to go for the Github Archive available here, you can then download json files representing Github Events on a daily/hour basis.

What is this about?

This script parses the git log and outputs Cypher statements to create a Neo4j database of your git history.

BEGIN
create constraint on (c:Commit) assert c.sha1 is unique;
COMMIT
BEGIN
CREATE (:Commit {author_email:'[email protected]',date_iso_8601:'2014-05-22 20:53:05 +0200',parents:['b6393fc9d5c065fd42644caad600a9b7ac911ae2'],refs:['HEAD', 'origin/master', 'master', 'in-index'],sha1:'934cacf9fe6cd0188be642b3e609b529edaad527',subject:'Some commit message',timestamp:'1400784785'});

	{"title":"AUTOGRAPH","definition":"The handwriting of any one.","related":["EX SCRIPTIS OLIM VISIS","MAYN L","WILL HOLOGRAPHIC","IDENTIFICATION","HOLOGRAFO","EX VISU SCRIPTIONIS","ONOMASTIC","HANDWRITING EXPERT","BON FR","COMPARISON OF HANDWRITING"]}
	{"title":"AUXILIUM REGIS","definition":"In English law. The king’s aid or money levied for the royal use and the public service, as taxes granted by parliament.","related":["BANCUS REGIS","VIDUA REGIS","AUXILIUM","SERVIENS DOMINI REGIS","DE NON RESIDENTIA CLERICI REGIS","AD JURA REGIS","CAMERA REGIS","MINISTRI REGIS","NON-RESIDENTIO PRO CLERICO REGIS","AUXILIUM CURIAE"]}
	{"title":"AVER SILVER","definition":"A custom or rent formerly so called.","related":["GREEN SILVER","AVER CORN","HERRING SILVER","ALE SILVER","SLOUGH SILVER","AVER, N","AVER, V","AVER","AVER PENNY","AVER LAND"]}
	{"title":"AVOID","definition":"To annul; cancel; make yoid; to destroy the efficacy of anything.","related":["CONDITIONS TO AVOID","VITIATE","ANNUL","VACATE","DESTROY","DISSOLVE","DISCHA

	[
	{
	"name": "Afghanistan",
	"topLevelDomain": [
	".af"
	],
	"alpha2Code": "AF",
	"alpha3Code": "AFG",
	"callingCodes": [
	"93"

	[{"id":1,"first_name":"Amalita","last_name":"Doers","email":"[email protected]","gender":"Female","ip_address":"214.190.147.250","avatar":"https://robohash.org/occaecatifacilisea.png?size=50x50&set=set1","username":"adoers0"},
	{"id":2,"first_name":"Linnie","last_name":"Blaxland","email":"[email protected]","gender":"Female","ip_address":"94.220.143.47","avatar":"https://robohash.org/dictaeaab.bmp?size=50x50&set=set1","username":"lblaxland1"},
	{"id":3,"first_name":"Foss","last_name":"Kerwen","email":"[email protected]","gender":"Male","ip_address":"4.127.37.8","avatar":"https://robohash.org/laboriosamexercitationemvel.jpg?size=50x50&set=set1","username":"fkerwen2"},
	{"id":4,"first_name":"Marijn","last_name":"Gosden","email":"[email protected]","gender":"Male","ip_address":"127.53.167.194","avatar":"https://robohash.org/ipsamquierror.bmp?size=50x50&set=set1","username":"mgosden3"},
	{"id":5,"first_name":"Rene","last_name":"Gamble","email":"[email protected]","gender":"Male","ip_addre

	import requests
	import json
	from os import makedirs
	from os.path import join, exists
	from copy import copy
	import requests
	LIST_ENDPOINT='http://api.viewers-guide.hbo.com/service/charactersList'
	FEATURED_ENDPOINT='http://api.viewers-guide.hbo.com/service/charactersFeatured'
	DETAIL_ENDPOINT='http://api.viewers-guide.hbo.com/service/characterDetails'
	DEFAULT_PARAMS = {'lang': 1}

	Unusable on spinning disk, these results are from an SSD.

	1M x create node+rel in 38s, 26k r/s
	1M x create 2 nodes, 2 relationships, 2 properties in 47s, 21k r/s -> 80k records / s
	1M x create 2 nodes with labels, 2 rels, 0 properties in 140s, 7k r/s
	100k x create 100 rel + node -> 10M in 20s, 5k r/s
	1M lookups by id in 22s, 43k r/s
	1M lookup by id compiled runtime in 21s, 47k r/s

	import json
	from itertools import combinations

	import requests
	import networkx as nx


	def get_senate_vote(vote):
	# Year can be replaced to fetch votes from different years (e.g., 2013). 1989 is used
	# as an example.