Dear fellow researchers,
I am currently writing my Bachelor's Thesis on the topic of dynamic network visualisation of an entrepreneurship social network in a city in Germany. Being relatively new to the whole topic and method, I am at a loss as how to best approach the topic. I am supposed to identify the key actors in the network and how they are connected using data available online (e.g. social media, newspaper and magazine articles, Crunchbase etc.).
I though about starting with Crunchbase to identify the actors, and then follow-up links to see if I can find who they are connected with. I also thought about searching though social media using relevant hashtags and identifying the actors and then going from there. However, I'm not sure if this is the right way to go.
I want to use Gephi as a network visualisation tool, but I am also unsure how to properly approach the data collection in order to have some sort of dynamic network as a result.
I hope you can help me and give me some tips or suggestions.
Ena
Dear Ena,
please note similar question was asked before - try to trail this conversation https://www.researchgate.net/post/Is_there_an_interactive_social_network_visualization_tool_that_enables_readers_to_zoom_in_and_out .
Moreover - have a glimpse of an eye here too https://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.html
Hope it helps!
Dear Ena
If I understood your question properly, the main bottle-neck is data collection and organization. I am not familiar with Crunchbase you plan to use, but I guess (since you chose it) it has all needed information for your study. In this case, the next step will be to properly organize the data your will get to a network format.
Than more data you will get, than more powerful the results will be. So think about what kind of "actor" properties you need and want to present to characterize them. Think also about properties of the "links", they also can help you to make some point.
When you have these data, assemble it to the network format, and then you can use Cytoscape or Geph, or other tools suggested by Anna. They both are very good tools to visualize networks and analyze them using Graph theory. They also provide very nice and wide options for visualization of the results.
I strongly recommend using R for data collecting, cleaning, analysing, and visualisation. In particular the igraph package is great but there are may extensions for dynamic visualisations. What is more important is the research question. What precisely do you want to address with this type of network analysis? That determines what type of data you will need and what type of tools to use.
Thank you everyone for your helpful suggestions.
I've already started data collections and so far it looks good in Gephi.
It's just going a bit slow, since I have to manually import the data, but I have enough time.
Thank you once again!
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