Another Wednesday, another Just KNIME It! challenge! This time we will explore KNIME’s network mining capabilities: did you know that you could also explore them the low-code way with our platform?
Diplomacy and international relations are inherently complex, with each country maintaining its own unique network of relationships with others. For non-Europeans planning a trip to Europe, obtaining a Visitor Visa may be required—and acceptance rates can vary significantly depending on the applicant’s nationality. Let’s explore which pairs of countries show higher acceptance or refusal rates for Visitor Visa applications across Europe.
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 JKISeason4-26 .
Need help with tags? To add tag JKISeason4-26 to your workflow, go to the description panel in KNIME Analytics Platform, click the pencil to edit it, and you will see the option for adding tags right there. Let us know if you have any problems!
Find herewith my Submission : Comparative Top and Bottom acceptance rates taken into count while dataset throws the requirement of understand the nomenclature of visa and its consideration. For the challenge requirement the selection of specific EU countries TOP and Bottom K non EU applyign countries has been taken for network comparative. JKISeason4-26 – KNIME Community Hub
Thanks @jproudfoot111 to have an example about Networks creation and config, that helped me. Especially about features etc.
For that challenge I proceed as follow:
Read the file
Handle missing values, especially for the Shengen State
Get unique pairs of countries - at least one is in Europe anyway as it is a list for Shengen countries
Using an expression to get the acceptance rate, the pair key (not mandatory) and to round the rates in % with 2 digits after comma
Get the list of countries and give the choice to the use to select one of the country
Get top 5 acceptance and refused country and assign colors and size (for the nodes of the network)
Create the network with and add features, such as node color, node size (based on acceptance) and main node (Shengen state) size.
Visualize
I noticed in the data that some visitors have visas issued from the country they want to visit. For example, Denmark has Uniform visas from Denmark. Should be issued electronically maybe? Or not sure how it works.
Anyway, I love this kind of visus as it is easy to understand.
And with these types of data, so many things can be represented and explored.
I think there is a niche business case to use network mining, but I do not see that (maybe just yet). But it was really good to deep dive into another useful gadget!
I have to admit I had no idea about KNIME’s networking mining capabilities so shout out to @jproudfoot111 ! I had to leverage from his workflow to understand the context of how this networking mining nodes function in KNIME…and it’s kinda of amazing.
These network mining tools with a widget selector starts to shine when you include multiple countries to understand visa acceptance/rejection patterns. When selecting Austria and Germany you can see that both countries share a similar rejection of visas from Senegal. That seems like an interesting pattern to me.
I think @berti093 makes a valid point in terms of understanding how to use network mining tools in a business use case but it’s exercises like these that at least get us introduced to these tools with these deep dive challenges and eventually the application of a business use case presents itself in the future.
Our solution to last week’s Just KNIME It! challenge is out!
Last week you all had the opportunity to sharpen your network mining skills while analyzing networks of countries and Visitor Visa acceptance and rejection rates in Europe. As some of you may have noticed, the backbone of this use case could be re-used in many different problems where the relationships between entities need to be assessed. Thanks for the really cool solutions and visualizations!
We come back tomorrow with another challenge related to trips in Europe. Once again we will leverage KNIME’s network mining capabilities, but this time the focus will be on itineraries that can be completed within distance constraints. Starting from a given city, which other places can you visit within, say, 200km by car? We can’t wait to see how you’ll solve this!
Another Wednesday, another Just KNIME It! challenge! This time we will explore KNIME’s network mining capabilities: did you know that you could also explore them the low-code way with our platform? 
Diplomacy and international relations are inherently complex, with each country maintaining its own unique network of relationships with others. For non-Europeans planning a trip to Europe, obtaining a Visitor Visa may be required—and acceptance rates can vary significantly depending on the applicant’s nationality. Let’s explore which pairs of countries show higher acceptance or refusal rates for Visitor Visa applications across Europe. 
Need help with tags? To add tag JKISeason4-26 to your workflow, go to the description panel in KNIME Analytics Platform, click the pencil to edit it, and you will see the option for adding tags right there. 
Let us know if you have any problems!
As some of you may have noticed, the backbone of this use case could be re-used in many different problems where the relationships between entities need to be assessed. Thanks for the really cool solutions and visualizations! 
We come back tomorrow with another challenge related to trips in Europe. Once again we will leverage KNIME’s network mining capabilities, but this time the focus will be on itineraries that can be completed within distance constraints. 
Starting from a given city, which other places can you visit within, say, 200km by car? We can’t wait to see how you’ll solve this!