Solutions to “Just KNIME It!” Challenge 08 - Season 2

:boom: New Wednesday, new Just KNIME It! challenge! :boom:

This week, our challenge explores the EuroVision competition :notes: using network mining techniques :gear:. Give it some thought!

Here is the challenge. Let’s use this thread to post our solutions to it, which should be uploaded to your public KNIME Hub spaces with tag JKISeason2-8 .

:sos: Need help with tags? To add tag JKISeason2-8 to your workflow, go to the description panel on the right in KNIME Analytics Platform, click the pencil to edit it, and you will see the option for adding tags right there. :slight_smile: Let us know if you have any problems!


Hi!..I think this is my worst Workflow i did till now…but it should guarantee what requested…
Anyway…next days i’ll try to improve it in order to get a better visualization.



here is my try:

I decided to delete the attribute “Points type” and aggregate rows. Visualisation has directed edges with the points received.

Have a nice evening,
Raffaello Barri


Hello KNIMErs, Here is my Solutions for “Just KNIME It!” Challenge 08 – Season 2


Hi all,
Here is my solution.

The points earned were used as the node size.
The winning country is shown in yellow.


Hello everyone. Here is my solution, where I also decided to go a bit beyond the basic requirements of the assignment and created a pipeline to insert the data into Neo4j — a native graph database.
This approach is useful from multiple perspectives:

  • the data is already represented in the graph
  • you can visualize it pretty much the same way as the assignment asks for
  • it possible to use graph data science algorithms
  • it helps to easily answer such important question as who liked both Portugal and Estonia the most in 2018.

Here’s my solution.

1 Like



Hello everyone :slight_smile:

For challenge 8, I have decided to show the network in two different ways:

  1. Using the -Network Creator-, -Object Inserter- and -Network Viewer- nodes:

  2. Using the -Generic JavaScript View- node to create Chord Diagrams:

Points Given By Countries:

Points Received By Countries:

The Chord Diagram is a really useful way to create a network that shows the relationship between pairs of countries and their voting choices. You can hover over one particular country on the Chord Diagram to more easily see the network for that country:

After learning how to create a movie in the last challenge, I have decided to use the same set of nodes to create movies of the evolving points distribution over the years.

Points Given:

Points Received:

You can find my workflow on the hub here:

Hope you enjoy it :slight_smile:



Hello KNIMErs

I was already working on my take with ‘Multiple-Group Chord Diagram’ ; when @HeatherPikairos ’ solution just popped up in challenge’s topic. Just congrats for the great work and very clean Java charts.

For this approach I am also using a Chord Diagram, but maybe more focused on analysis. I’ve had to code it in R, as my Java skills just trend to 1E-2 . R ‘circlize’ package has a lot of fun as colours in chart are random.

In the way of @HansS , I started to question myself ‘What If … ?’

  • If based on just Northern Europe’s votes (FIN, GBR, ISL, LTU, NOR, and SWE); would the winner be the same in rotterdam-2021? :thinking:
  • Does the jury share tastes with televoters? :upside_down_face:

Well, the podium chart is in the Dashboard as well. However if curious I can give you a clue… :face_with_monocle:

Enjoy coding!


My Take on the challenge 8 on network … Network diagram is effective on direction but parallal coordinate plot is more insightful… wish we have a sankey node too… or PCP can be exdended to sankey.


Chord is more effective… outcome too aesthetically appealing and insightful. :clap:


Hi all,
Here is my solution.

I guess total points since the first year would give kind of voting trends by country.
Node size means sum of points given by a country you selected, line width, sum of points of “From” or “To”. Blue and red lines mean “From” and “To” respectively.


Some debugging in podium chart and an added Sunburst has happened since my lastest post. The reason I am not working with Network Mining nodes is because I already experimented with them in JKI S1 CH35 (LinkedIn Network Graph), and I like to experiment with visualizations that I hadn’t used before.

This is a capture for the same dashboard query configuration as it looks like now:

Did you notice there were invited countries from overseas in previous editions?
I am now updating the chart dictionaries to sectorize those sub-regions.

Happy analysing!


Thanks @gonhaddock Congratulations for your R version :slight_smile:


Disfruto más que un :pig2: en un charco :rofl:
:black_small_square:He querido implementar para seguir aprendiendo del los nodos geoespaciales, los cuales, me parecen una autentica bestialidad. Igual en esta ocasión no era su mejor utilidad, pero aún así lo he disfrutado y sobre todo, he aprendido el potencial que tiene :top:

Nos vemos por el foro!


Hi all,Here is my solution.
Thank you for sharing the solution, which has enabled me to learn a lot of knowledge.


Tonight I’ve been working on the latest upgrade for the challenge workflow, just before submission deadline. I want to keep you updated on relevant changes in the code.

  • R Multiple Group Chord Diagram:
  1. Inner ISO_ocuntry labels have been moved to an external radial position, for a cleaner reading and bar size correlation.
  2. Diagram has been rotated 90 degrees (votes on the left hand side position, points to the right)
  3. Introduce chart color control based on RColorBrewer library palette.
  • A network view (Network Mining nodes based) has been added to the dashboard, aiming to fully accomplish with challenge description request.

Keep coding :computer:


Hi, KNIMEr :partying_face: :partying_face: :partying_face: :partying_face:

Here is mine:

If you have interesting, see my blog

Points worth mentioning

  • The number of countries participating in voting remained stable between 1975 and 2012.
  • Clustering countries based on voting patterns reveals similarities in cultural backgrounds and interests.
  • Network visualizations face challenges in observing a large number of nodes and lack directed graph options.
  • Germany and Greece exhibit similar voting patterns and Greece consistently awards high scores to Cyprus. Do you know why? :grin:



:nerd_face: As always on Tuesdays, here’s our solution to last week’s challenge! :nerd_face:

:notes:What did you think of our EuroVision challenge? We loved the visualizations we saw here!

See you tomorrow for a new challenge!