Graph analytics is a well established discipline in Academia and software industry. Relevant vendors include companies like neo4J and Tigergraph or open source solutions such as Cytoscape or Gephi.
I don’t expect Obsidian to be a graph analytics tool or a Knowledge Graph solution but it can borrow a lot of ideas from those two spaces. Here are some examples of how to use metrics to color a graph:
Betweenness centrality
Definition: Betweenness centrality measures the number of times a node lies on the shortest path between other nodes.
What it tells us: This measure shows which nodes are ‘bridges’ between nodes in a network. It does this by identifying all the shortest paths and then counting how many times each node falls on one.
When to use it: For finding the notes who influence the flow around the knowledge base.
EigenCentrality
Definition: EigenCentrality measures a node’s influence based on the number of links it has to other nodes in the network, how many links their connections have, and so on through the network.
What it tells us: By calculating the extended connections of a node, EigenCentrality can identify nodes with influence over the whole network, not just those directly connected to it.
These examples were adapted from: Cambridge Intelligence
Other metrics could be:
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Colour nodes (different shades of blue, for example) by the number of links from or backlinks to a note. (e.g. How many times is a research paper cited on other papers?)
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Number of different Tags (implying different topics).
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Note length (number of words).
The nodes could also be coloured according to categories:
We would need a proper interface and metadata to handle these classifications.
Glad to work with anyone willing to develop a Plugin / CSS if that’s the fastest solution to have this available. I can’t code but can help test and tweak someone else’s code if needed.