What Graph Analysis of Wikipedia Tells Us About the Relevancy of Recent Knowledge

The chart below was generated using data analyzed with a Neo4j Graph Database and Apache Spark GraphX. 10.9 million Wikipedia articles and 110 million hyperlinks were analyzed to produce a PageRank and Triangle Count for each node in the graph. The Triangle Count metric is a measure of clustering, while the PageRank metric is a measure of relevancy.

Knowledge moves forward in time

Every year through 1850—2012 on the X-axis represents a Wikipedia page that describes historical events and facts about that calendar year. Link analysis was performed on the inbound and outbound hyperlinks for each page and all other pages in the graph that contribute to that page's relevancy.

The chart describes a probability distribution over time. This distribution indicates that if a person were to randomly click hyperlinks starting from any page on Wikipedia, the person would move towards articles with a higher closeness centrality to Category:Year pages occurring later in the timeline.

When it comes to our collective human knowledge, as time moves forward, distant history becomes inversely relevant to more recent events in our timeline.

To see this pattern you can click and drag areas of the chart to zoom in. You'll notice the pattern is local as well as global.

Why is the year 2000 so relevant?

Wikipedia, the world's largest encyclopedia of human knowledge, was first launched on January 15th, 2001.

Links

• Chart source code: jsfiddle/highcharts
• In an upcoming blog post I'll walk you through launching an EC2 Spark Cluster with a Wikipedia dataset in Neo4j. Stay tuned!
• If you're interested in learning more about how I computed the graph metrics for this chart, take a look at this blog post about how to extend Neo4j to do big data analysis with Apache Spark GraphX

A Docker Image for Graph Analytics on Neo4j with Apache Spark GraphX

I've just released a useful new Docker image for graph analytics on a Neo4j graph database with Apache Spark GraphX. This image deploys a container with Apache Spark and uses GraphX to perform ETL graph analysis on subgraphs exported from Neo4j. This docker image is a great addition to Neo4j if you're looking to do easy PageRank or community detection on your graph data. Additionally, the results of the graph analysis are applied back to Neo4j.

This gives you the ability to optimize your recommendation-based Cypher queries by filtering and sorting on the results of the analysis.

Photo credit AMPLab Berkley

Using Apache Spark and Neo4j for Big Data Graph Analytics

As engineers, when we think about how to solve big data problems, evaluating technologies becomes a choice between scalable and not scalable. Ideally we choose the technologies that can scale to a variety of business problems without hitting a ceiling down the road.

Database technologies have evolved to be able to store big data, but are largely inflexible. The data models require tedious transformations and shuffling around of data. This is a complex process that is compounded in its complexity by combining a variety of inflexible solutions and platforms.

Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.

Still, where does all that data come from? Where does it go when the analysis is done?