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People still upvote hairball graphs every time. Fortunately there is a cure:

https://cambridge-intelligence.com/how-to-fix-hairballs/




The hairball was much worse before. I used a lot of techniques from this paper [1] to make it look decent and a bunch of other heuristics based on other papers to make it look informative.

[1] https://jgaa.info/accepted/2015/NocajOrtmannBrandes2015.19.2...


I'd give it a shot to make node embeddings with Node2Vec [1] and then reduce them to 2D with UMAP [2]. I think it could help breaking apart the hairball, assuming you have a nice clustered structure.

[1] https://pytorch-geometric.readthedocs.io/en/latest/generated... [2] https://umap-learn.readthedocs.io/en/latest/


Also edge bundling can help. See the papers by Benjamin Bach et al.

https://aviz.fr/~bbach/confluentgraphs/


Is graph data processing considered visualization style? It is changing the data, how can this be considered "visualization"?


We aren't changing the data, just changing how the graph is displayed.


The relationship of data is part of the data.

Your visualisation tool may require it in a specific format, but it still about properties of your data.




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