1. Method:
    1. articles published between June and December 2012 by major news outlets were scraped from the web.
    2. A dictionary of all named entities was produced to later recognize names of people and companies from text.
    3. An associative network was produced in this fashion: each article containing the word "Patria" and all its forms was processed paragraph by paragraph. A list of named entities was extracted per paragraph. These entities were all connected in a subgraph. Then the subgraph was integrated into master graph, keeeping date information intact.
    4. The master graph,containing information (all entities and relationships) was visualized as a dynamic network in Gephi and animated.

    blog: http://virostatiq.com/

    # vimeo.com/67992584 Uploaded 59 Plays 0 Comments
  2. Method:
    1. articles published between June and December 2012 by major news outlets were scraped from the web.
    2. A dictionary of all named entitties was produced to later recognize names of people and companies from text.
    3. An associative network was produced in this fashion: each article containing the word "corruption" and all its forms was processed paragraph by paragraph. A list of named entities was extracted per paragraph. These entities were all connected in a subgraph. Then the subgraph was integrated into master graph, keeeping date information intact.
    4. The master graph,containing information (all entities and relationships) was visualized as a dynamic network in Gephi and animated.

    blog: http://virostatiq.com/

    # vimeo.com/67992583 Uploaded 1,759 Plays 0 Comments
  3. Animation of dynamic network containing transactions between private publishing houses and state institutions.

    Node radii are proportional to their respective network in-degrees (number of incoming connections / number of transactions with state institutions) and change over time.
    Big dots are publishing houses, small dots are state institutions that pay them for various services.

    Animation shows which private businesses are best connected with institutions of Slovenian state (schools, libraries, universities, ministries, etc.)

    Graph edges (connections between nodes) are not shown due to their large number, but act as attraction forces between nodes. Each edge represents a transaction. Edge duration is proportional to transaction sum.

    Data source: Supervizor (http://supervizor.kpk-rs.si/)
    Author: Marko Plahuta
    Tools: Apache Solr, Gephi, Java

    blog: http://virostatiq.com/

    # vimeo.com/67340265 Uploaded 113 Plays 0 Comments
  4. Blog post and interactive versions here: http://virostatiq.com/social-network-diagrams-of-slovenian-governments-between-2001-and-2013/

    # vimeo.com/70494286 Uploaded 753 Plays 0 Comments

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