NetLogoVisualConversion is a tool (written in Python 2.7) that helps you to use your NetLogo created networks in many Network Visualizing tools (like Gephi, Guess or similar)

NetLogo model:

is converted into:

Outpot of script is a GraphML format script that you can immediatelly import to your favourit visualization model and continue processing, calculation metrics or visualization of course :)

There is a way to export your NetLogo network through nw netlogo extension, but there are many reasons why it doesn't do acceptable enough work:

• you need to know NetLogo and be invisive to NetLogo model in order to export network
• position of turtles/nodes is "random"
• colors are not preserved
• additional parameters stored in turtles, links are not preserved
• etc...

Here are some reasons why you want to use NetLogoVisualConversion script:

• recover all parameters of your netlogo world in Gephi
  • turtles -> nodes
  • links -> edges
  • nodes positions are preserved
    • you can keep and observe a specific topology that is created through your NetLogo model
  • nodes sizes and node colors are preserved
    • it is very useful if they reflect some internal network characteristics
  • edge weights are preserved
  • breeds of turtles and edges are preserved
    • you can use that to encode special node/edges characteristics, like node opinions, or type of connections between nodes, etc
• intermediate igraph model
  • if you do a bit more of Python programming, you can extend script and play with intermediate igraph model that we go through before exporting to GraphML document
• translation parameters
  • nodes sizes scaling
  • nodes positions scaling
  • edges weights scaling
  • ignoring edge weight
  • prefixing node names

If you think this is useful thing please star it

If you think that features from the following list would be nice to have, please let me know

• Online tool
  • You wouldn't need to install any tool on your machine
  • you would be able to upload NetLogo world model and download visualized graph
• GraphML -> NetLogo
  • support for importing GraphML model into NetLogo
  • You might need it if you have a complex network that you want to start your simulation with
• NetLogo over time/ticks -> dynamic (longitudinal) netowrk
  • it will track your network (turtles+links) changes over time and record it into dynamic network that you can visualize/animate in Gephi, etc
• Real-time visualizing of currently running NetLogo model in Gephi (or other compatible tool)
  • it will provide a way to make a real-time connection between running NetLogo model and Gephi
  • any change happening in NetLogo would in the real time update in your graph in Gephi
  • Ask for this if you REALY, REALY need it, and please
  • Please explain why do you need it :)

please do:

• Let me know if you would benefit from it and I will try to make it
• fork this project and do work on your on and later ask me to merge
• use this code to make something new

NetLogo model:

is converted into:

This model and documentation was adapted by Eytan Bakshy and Lada Adamic from: Wilensky, U. (2005). NetLogo Small Worlds model. http://ccl.northwestern.edu/netlogo/models/SmallWorlds. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

NetLogo model:

is converted into:

Here we preserved color and size of nodes from original NetLogo model.

Original node size and color are very important here since color identifies which nodes are infected and size represent the responsibiliy in infection difussion.

Copyright 2008 Uri Wilensky. Modified by Lada Adamic 2009.

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at uri@northwestern.edu.

NetLogo model:

is converted into:

This model was adapted by Lada Adamic from Uri Wilensky's Giant Component model in NetLogo's models library http://ccl.northwestern.edu/netlogo/models/GiantComponent.

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

NetLogo model:

is converted into:

or after applying ForceAtlas 2 layout:

This model was adapted by Lada Adamic from Uri Wilensky's Giant Component model in NetLogo's models library http://ccl.northwestern.edu/netlogo/models/GiantComponent.

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

NetLogo model:

is converted into:

Created by Lada Adamic 2008

You need to have:

1. NetLogo, unless you have NetLogo world already exported
2. Python 2.7 installed, it should work with Python 3 as well
3. iGraph package

Exporting NetLogo World from NetLogo is straightforward:

1. Open NetLogo
2. Load NetLogo model
3. Execute it and stop in the moment you want to take snapshopt
4. Go to the menu and choose: File > Export > Export world ...
5. Save file
   1. The best place to save your file is into the data folder of the folder containing the NetLogoVisualConversiontool tool
   2. If you stored it to the data folder and named it world.csv then you can convert it by passing --filein "data/world.csv" to the tool

You can download the NetLogoVisualConversiontool tool by clicking on the ZIP button on the top of the page or simple following the link:

Download link

after downloading NetLogoVisualConversion tool

1. unpack it
2. go to shell and
3. navigate to the folder containing unpacked tool (stay in it, do not go to the src folder)

run the tool in the following way:

python src/convert.py --help

You will see a list of parameters you can provide.

1. Start Gephi
2. Choose import from menu: File > Open ...
   1. Choose file you want to import
   2. Unselect Auto-scale checkbox (otherwise node sizes will be lost, etc)

If you run the tool in the following way:

python src/convert.py --help

You will see a list of parameters you can provide.

(NOTE: If you would benefit from some other parameter, please let me know)

If you run it simply as python src/convert.py you will get first missing parameter and so on, as long as there are missing parameters :)

Here are some examples of calling script:

python src/convert.py --phase convert --filein "data/examples/NetLogo/netlogo world - cliques.csv" \
    --fileout "data/examples/Gephi/conversions/converted - netlogo world - cliques.graphml"

converts (--phase convert) NetLogo network "data/examples/NetLogo/netlogo world - cliques.csv" into "data/examples/Gephi/conversions/converted - netlogo world - cliques.graphml"

python src/convert.py --phase convert --node_size_multiplyer 5 --edge_weight_multiplyer 3 --coord_multiplyer 10 \
    --node-name-prefix "N_" --filein "data/examples/NetLogo/netlogo world - cliques.csv" \
    --fileout "data/examples/Gephi/conversions/converted - netlogo world - cliques.graphml"

converts NetLogo network "data/examples/NetLogo/netlogo world - cliques.csv" into "data/examples/Gephi/conversions/converted - netlogo world - cliques.graphml" with few modifications:

• --node-name-prefix "N_" adds the N_ prefix to each node name
• --node_size_multiplyer 5 multiplies the size of each node by 5
• --edge_weight_multiplyer 3 multiplies the weight of each edge by 3
• --coord_multiplyer 10 multiplies the X and Y coordinate (scale up) of each node by 10

python src/convert.py --phase convert --node_size_multiplyer 5 --edge_weight_ignore true --coord_multiplyer 10 \
    --node-name-prefix "N_" --filein "data/examples/NetLogo/netlogo world - SmallWorldWS.csv" \
    --fileout "data/examples/Gephi/conversions/converted - netlogo world - SmallWorldWS.graphml"

Similar to the previous example except that it use different files and by

• --edge_weight_ignore true removes edge weight in resulting graph

Here are the conversion instructions for the rest of netlogo world examples you can find in data/examples/NetLogo/ folder.

python src/convert.py --phase convert --node_size_multiplyer 5 --edge_weight_multiplyer 3 --coord_multiplyer 10 \
    --node-name-prefix "N_" --filein "data/examples/NetLogo/netlogo world - OpinionFormationModelToy.csv" \
    --fileout "data/examples/Gephi/conversions/converted - netlogo world - OpinionFormationModelToy.graphml"

python src/convert.py --phase convert --node_size_multiplyer 5 --edge_weight_ignore true --coord_multiplyer 10 \
    --node-name-prefix "N_" --filein "data/examples/NetLogo/netlogo world - RandomGraphs-static-geo.csv" \
    --fileout "data/examples/Gephi/conversions/converted - netlogo world - RandomGraphs-static-geo.graphml"

python src/convert.py --phase convert --node_size_multiplyer 5 --edge_weight_ignore true --coord_multiplyer 10 \
    --node-name-prefix "N_" --filein "data/examples/NetLogo/netlogo world - RandomGraphs-rand-encounter.csv" \
    --fileout "data/examples/Gephi/conversions/converted - netlogo world - RandomGraphs-rand-encounter.graphml"

python src/convert.py --phase convert --filein "data/netlogo world - BADiffusion.csv" \n
    --fileout "data/converted - netlogo world - BADiffusion.graphml" --edge_weight_ignore true 

Please check in python src/convert.py --help for all other parameters.