Summary
An Obsidian plugin to find hidden connections between vault notes
Jots
Every PKM should have this functionality, or at least make it easy to extract enough information to analyze externally.
Analysis Types
NOTE
Extracted from the README
Co-Citations
Count the number of times two notes are linked in the same note, with extra weight for link proximity within the linking note.
Think of co-citations as a 2nd-order backlinks panel: Instead of showing where something is cited, it shows why, or with whom or what it is cited!
Similarity
Measure how similar two notes are based on their graph connections.
Jaccard Similarity
$$ J(A,B) = \frac{|A \cap B|}{|A \cup B|} = \frac{|A \cap B|}{|A| + |B| - |A \cap B|} $$where $|A|$ and $|B|$ are the set of connections (in either direction) for any two pages
Similarity functions - Neo4j Graph Data Science
Link Prediction
Measure the probability that two notes should connect to each other based on their connections in the graph.
Adamic Adair
[PDF] Friends and neighbors on the Web | Semantic Scholar
$$ A(x, u) = \sum\_{u \in N(x) \cap N(y)} \frac{1}{\log{|N(u)|}} $$where $N(x)$ is the number of neighbors of $x$.
Common Neighbors
$$ CN(x,y) = |N(x) \cap N(y)| $$where $N(x)$ is the number of neighbors of $x$.
Community Detection
Try to find groups of similar notes.
Label Propagation
Start by giving each node a unique label (its own name). Then, look at each node’s neighbours, and change it’s label to the most common among it’s neighbours. Repeat this process iterations number of times.
At the end, show the nodes grouped by the last label they had.
Clustering Coefficient
The ratio of the number of triangles the page is a part of to the number of triangles it possibly could have been part of.
$$ C\_{i} = \frac { |{ e\_{jk} : v\_{j}, v\_{k} \in N\_{i}, e\_{jk} \in E }| } { k\_{i}(k\_{i} - 1) } $$Related
Added to vault 2024-05-11. Updated on 2024-05-11