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Wikipedia graph mining: dynamic structure of collective memory
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Implementation of the graph learning algorithm presented in Wikipedia graph mining: dynamic structure of collective memory. The learning algorithm is inspired by the Hebbian learning theory.

We also reported the results with interactive graph visualizations in an accompanying blog post.


To reproduce the experiments, download the dataset from DOI.

Clone this project and extract the downloaded .zip files to /src/main/resources/ folder.

Change PATH_RESOURCES in Globals.scala to the path to this project on your computer.

Runing the experiments

Open WikiBrainHebbStatic.scala and run the code (Shift+F10 in Intellij Idea).

You may have to change your Spark configuration according to RAM availability on your computer.

val spark = SparkSession.builder
  .master("local[*]") // use all available cores
  .appName("Wiki Brain")
  .config("spark.driver.maxResultSize", "20g") // change this if needed
  .config("spark.executor.memory", "50g") // change this if needed

Resulting graphs

You will find the resulting graph graph.gexf in PATH_RESOURCES. This file can be opened in Gephi.

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