Thinking with your Zettelkasten 🧠 an intro to Obsidian’s local graph view

Local graph view in Obsidian turns a growing slip box into a usable “mind map,” letting ideas cross-pollinate so new research angles emerge—especially when inspiration comes from the connections (and missing connections) between notes.

The workflow starts with a core distinction in the slip box method: documentation (note-taking) stores what’s learned, while creation (note-making) generates new thinking by linking. Morgan’s approach treats note-making as an iterative linking session. Instead of relying on the default alphabetical list of notes—which is “linear” and hard to navigate—she uses Obsidian’s local graph view for a single note to see a compact network of related ideas. The graph places the selected note at the center and shows one-link-away neighbors as nodes connected by lines, effectively creating a mini mind map that can spark new combinations.

A typical session begins by identifying a theme that’s already appearing across notes. For example, repeated references to “failure” eventually become a dedicated note. That note initially functions as a hub, but the backlinks list is still hard to interpret because it doesn’t reveal relationships among the surrounding ideas. Local graph view fixes that by showing the immediate neighborhood of the “failure” note, and then—crucially—by enabling “neighbor links” in the graph settings. With neighbor links turned on, the graph reveals connections among the neighboring nodes, not just links to the central idea. This makes it possible to spot gaps and opportunities: some notes about failure are connected to failure, but not connected to each other in ways that would make sense for future writing.

From there, the linking happens in two modes. First, she adds missing links between existing notes when the graph reveals that two relevant ideas were created at different times and never connected. Second—and more creatively—she uses the graph to generate new ideas by combining concepts that weren’t previously linked. A concrete example starts with a note called “you can’t cheat at juggling,” which initially sits alone in the failure neighborhood. She increases link depth to explore, then drills into that juggling note while keeping the graph’s filter context consistent. A quote attributed to Penn Jillette about learning and success conditions leads her to connect juggling’s clear “conditions for success” to life skills, including a note on “fear of failure.”

That chain expands further: fear of failure links to perfectionism, which links to procrastination and “never living out your dreams.” The result is a reframing of failure—from something to fear into something to respect because it’s part of how goals are achieved. She then creates a new note about the idea that improving at juggling requires frequent failure, and uses the updated local graph to guide the next connections. She also adds a new intersection note connecting “you can’t cheat at juggling” and “fear of failure,” and then creates additional supporting notes such as “failure teaches.”

When a note has too few connections to function as a brainstorming hub, she adjusts link depth (e.g., from one to two) to enlarge the local neighborhood and increase the chance of accidental, useful links. When a note has enough momentum, she warns that overly complex maps stop being inspirational; at that point, she switches to exploring smaller subgraphs from connected notes.

By the end, the graph view is positioned as a practical antidote to overwhelm: it makes idea generation feel less scary by turning a complex dissertation landscape into navigable clusters of related notes. The method also includes “placeholder” links—creating references to notes that don’t exist yet so they appear in the graph as dark nodes, ready to be turned into real notes later.