Network Analysis and Visualization in Spark using GraphX

We use the Apache Spark GraphX API to calculate graph information within AUT. This can complement analysis done in Gephi.

Calculating Dynamic PageRank

Here is a script that calculates dynamic PageRank on a WARC file:

import io.archivesunleashed.spark.rdd.RecordRDD._
import io.archivesunleashed.spark.matchbox.RecordLoader
import io.archivesunleashed.spark.matchbox.ExtractGraph

val recs=RecordLoader.loadArchives("path/to/warc.gz", sc)

val graph = ExtractGraph(recs, dynamic=true)
graph.writeAsJson("/path/to/home/nodes-test", "/path/to/home/links-test")

ExtractGraph Explained

ExtractGraph implements two versions of the PageRank version: a dynamic version, which iterates over the graph until scores converge within a specified convergence value, and a static version which runs for a fixed number of iterations. The version is specified by the boolean dynamic parameter. The parameters tolerance and numIter are optional (the former defaults to 0.001, the latter to 3).

Dynamic PageRank example: val graph = ExtractGraph(recs, dynamic = true, tolerance = 0.0001)

Static: val graph = ExtractGraph(recs, dynamic = false, numIter = 4)

Understanding Results

Results take the form as following:


{"domain":"","pageRank":0.15602109375,"inDegree":1,"outDegree":0} {"domain":"","pageRank":0.1548755648910227,"inDegree":1,"outDegree":27} {"domain":"","pageRank":0.1805159076887007,"inDegree":29,"outDegree":0}


{"date":"20060622","src":"","dst":"","count":1072} {"date":"20060622","src":"","dst":"","count":367} {"date":"20060622","src":"","dst":"","count":267} {"date":"20060622","src":"","dst":"","count":220}

Visualizing Results in a Browser with D3.js

We have a built in link visualizer, built in D3.js. This was developed by the amazing team of Alice Ran Zhou (University of Waterloo), Jeremy Wiebe (University of Waterloo), Shane Martin (York University), and Eric Oosenbrug (York University), in part during the Archives Unleashed hackathon. You can see their commit history in this repository.

The D3.js visualizer looks like this:

Example of the D3.js visualizer

You can find it in aut/vis/link-vis. This page shows you two things: how you can load in sample data and visualize it, and then how you can replace the sample data with your own.

Using Sample Data

To test it out, navigate to the aut/vis/link-vis directory on your command line. You can then complete the following steps:

  1. Create a new directory labelled data, which will have the full path of `aut/vis/link-vis/data.
  2. Copy the graph.json file from the aut-resources directory into data.
  3. Run python -m SimpleHTTPServer 4321 from your aut/vis/link-vis directory. If you are using Python3, you may need to run python -m http.server 4321 instead.

You can then navigate to localhost:4321 in your browser.

Generating Your Own Data

The visualizer requires your data to be in a particular format. To do so, we use jq to combine the NODES and LINKS data as above. You can download jq here.

$ jq -c -n --slurpfile nodes <(cat nodes/part-*) --slurpfile links \
  <(cat links/part-*) '{nodes: $nodes, links: $links}' > graph.json

Replace your old graph.json with this new one and you can explore your data in a browser!