How to leverage Logseq's linked structure?

Questions & Help
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When graphs can talk

For having a visual graph communicate something to us, we should consider its signal and noise:

  • Each connection between nodes:
    • carries some signal
    • adds noise to the nearby connections
  • That makes a graph (or subgraph) with:
    • too few connections: silent, has nothing to say
    • too many connections: noisy, has nothing meaningful to say
  • As a (simplified) consequence, graphs (or subgraphs):
    • that look like stars (or stars of stars), are neat but silent
    • where everything connects with everything else, are noisy
    • that combine the above, combine silence with noise
    • that balance their connections, may communicate something
  • To improve the situation:
    • look for nodes that are:
      • underconnected:
        • have less than 2 connections
          • they should either:
            • acquire more connections
            • be merged into other pages
      • overconnected:
        • have more than 8 connections
          • they should remove some connections by either:
            • delegating them to the remaining ones
            • breaking into smaller pages
        • bridge things that are directly connected to each-other
          • this adds noise
          • decide which are the direct connections and remove the indirect ones
    • use the following soft rule to add missing connections or even nodes:
      • if node A has:
        • a connection of type R with node B
        • a connection of type S with node C
      • and R is orthogonal to S
      • then some node D has:
        • a connection of type S with node B
        • a connection of type R with node C

Structure matters

The streets on a map originate from either:

  • animals (e.g. in an old forest)
    • they connect points of interest (water etc.)
  • engineers (e.g. in a modern city)
    • they essentially connect doors

They both are useful in going from one point to another.

  • If I structure a graph as:
    • an index, it will be easier to find the inserted info
      • think of multiple doors in parallel corridors
    • a hierarchy, it will be easier to navigate that hierarchy
      • think of narrow streets connecting to larger ones, to avenues, to highways
  • Structures like the above, give me back exactly what I put in.
    • This is great for returning to a place.
  • If I let a graph structure itself, it may give me back more, both:
    • from its structure
      • think of ants’ tracks
    • from its lack of structure
      • It is a comparison between expectations and the actual graph.
        • Whenever the expectations fail and they are:
          • right, it is a chance to fix the graph
          • wrong, it is a chance to learn something

A simplified example

  • Consider a single journal-note:
    • [[Purchase]] of [[apples]] by [[my neighbor]].
  • Here are some easy questions I could ask, related to that note:
    • What did my neighbor purchase?
    • Who did purchase apples?
    • What happened between apples and my neighbor?
  • Here is a less easy question I could ask:
    • What are the transactions between persons and fruits?
  • To answer that, some extra knowledge is needed:
    • my neighbor is a person
    • an apple is a fruit
    • a purchase is a transaction
  • But I still query for pre-existing notes:
    • I entered some transactions in the past.
    • I query for those transactions in the present.
    • This is the traditional boring approach.
  • Now consider a graph that contains no transactions at all.
    • This is not a necessity, but helps with focus.
  • Then consider the following hard question:
    • What could my neighbor be involved in?
  • And here is a desired answer:
    • My neighbor could purchase apples.
  • The desired answer doesn’t exist in any individual note.
    • I query for possible transactions in the present.
    • I may enter those transactions in the future.
    • I may find that some have already happened in the past, but I missed them.
  • To provide such an answer, even more knowledge is needed:
    • a person is an agent
    • a fruit is an object
    • a transaction involves an agent and an object
      • this is the type of knowledge that can lead to discoveries
  • One could try working this answer by combining the previous knowledge:
    • my neighbor is a person, therefore an agent
    • apple is a fruit, therefore an object
    • a purchase is a transaction, therefore involves an agent and an object
    • Datalog anyone?
  • Alternatively, one could work this answer by following connections in a well-made graph:
    • There is a path from my neighbor to purchase.
    • There is a path from purchase to apple.
    • Nobody constructed those paths on purpose, they emerged.
    • In a noisy graph, they remain invisible.

A simple visual example

Short points to keep

  • The right tool for the job:
    • Journals are for events.
    • Other pages are for knowledge.
    • It is not “one or the other”, both can co-exist.
  • Graphs don’t benefit from journals, but from interlinked pages.
    • Tagged journals generate stars.
      • We typically hide those.
    • Interlinked pages generate paths.
      • Excessive noise makes paths invisible.
    • Therefore, graphs don’t benefit from stars, but from visible paths.
  • Failed expectations can be:
    • opportunities
    • pleasant surprises
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