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https://github.com/SkepticMystic/graph-analysis
Analyse the structure of your Obsidian graph using various analysis techniques
https://github.com/SkepticMystic/graph-analysis
graph-algorithms obsidian-plugin
Last synced: 16 days ago
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Analyse the structure of your Obsidian graph using various analysis techniques
- Host: GitHub
- URL: https://github.com/SkepticMystic/graph-analysis
- Owner: SkepticMystic
- License: gpl-3.0
- Created: 2021-07-11T13:59:35.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-05-23T08:15:58.000Z (over 2 years ago)
- Last Synced: 2024-08-01T00:46:09.438Z (3 months ago)
- Topics: graph-algorithms, obsidian-plugin
- Language: JavaScript
- Homepage:
- Size: 30.7 MB
- Stars: 392
- Watchers: 13
- Forks: 19
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- my-awesome - SkepticMystic/graph-analysis - algorithms,obsidian-plugin pushed_at:2022-05 star:0.4k fork:0.0k Analyse the structure of your Obsidian graph using various analysis techniques (JavaScript)
- jimsghstars - SkepticMystic/graph-analysis - Analyse the structure of your Obsidian graph using various analysis techniques (JavaScript)
README
# Graph Analysis
Graph analysis adds the **analysis view** to Obsidian which implements a set of
algorithms that computes useful relations between the notes in your vault! Our
flagship algorithm is the **Co-citations** panel, that we describe as a _2nd
order backlinks panel_.
The Graph Analysis view shows a table of note names and numbers, each
representing the value of some graph analysis algorithm on that note in relation
to the current note.
e.g.
- `[[A]] is 0.9 Similar to [[B]]`
- `[[A]] has a 0.6 chance of being connected to [[B]]`
- `[[A]] is co-cited with [[B]] 6 times`
## Analysis Types
Graph Analysis currently has 4 different analysis types:
1. Similarity
2. Link Prediction
3. Co-Citations
4. Community Detection
Each implement different algorithms with different purposes.
### Co-Citations
Co-Citations counts the number of time two notes are cited together in the same
note and gives extra weight when the two notes are cited close together.
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!
For example, if `[[C]]` has `[[A]] and [[B]]` in its content, then `[[A]]` and
`[[B]]` will each have a co-citation of one.
Each note with co-citations > 0 is given a drop down menu. Inside each drop
down, you can see which note co-cites those two notes, and the sentence in which
they are co-cited (if in the same sentence), otherwise just the sentence with
the other link.
#### Example use case with daily notes
An example why this is useful is given by @HEmile:
> I use a lot of daily notes, in which I journal and write about the news of the
> day. This makes the backlinks panel a bit boring: It only shows on what dates
> I wrote about some note. The Co-Citations algorithm shows me much more! For
> example, the `Joe Biden` note shows me I usually write about Biden together
> with `Donald Trump`. But if I want to know what I wrote about the relations
> between Joe Biden and `China`, I can just look in the co-citations panel and
> expand the relation to see the story!
#### Video Tutorial
This video gives a longer and in depth overview for why Co-Citations is so useful!
[](https://youtu.be/rK6JVDrGERA)
### Similarity
Similarity is a measure of how similar two notes are based on their
connectedness in the graph (ie. note content is not considered). Currently, only
the Jaccard Similarity measure is implemented.
#### Jaccard Similarity
**Formula**:
[Source](https://neo4j.com/docs/graph-data-science/current/alpha-algorithms/jaccard/#alpha-algorithms-similarity-jaccard-context)
Where
- `|x|` is the number of neighbours the node `x` has (links going in or out).
- `|x & y|` is the number of neighbours that both `x` and `y` have in common
### Link Prediction
Link Prediction is a measure of the probability that two notes should be
connected based on their other connections in the graph. The implemented Link
Prediction algorithms are Adamic Adar and Common Neighbours.
#### Adamic Adar
**Formula**:
[Source](https://neo4j.com/docs/graph-data-science/current/alpha-algorithms/adamic-adar/)
Where:
- `N(x)` is the number of neighbours of `x`
#### Common Neighbours
**Formula**:
[Source](https://neo4j.com/docs/graph-data-science/current/alpha-algorithms/common-neighbors/)
Where:
- `N(x)` is the numbers of neighbours of `x`
### Community Detection
These algorithms 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
Gives the ratio of the number of _triangles_ the `u` is a part of, to the number of triangles it possibly _could have_ been a part of:
## Utility Classes
Each row in the graph analysis tables (or co-citations dropdowns) has a class:
`analysis-linked` or `analysis-not-linked`, indicating if the current note is
linked to the note in that row. This gives you the ability to style a table row
based on whether it's connectedness.
For example, you can make linked notes have a lower opacity:
```css
tr.analysis-linked {
opacity: 0.3;
}
```
You could even go so far as to hide linked rows completely:
```css
tr.analysis-linked {
display: none;
}
```
## Settings
In the analysis view, you have the option to choose between different
`Analysis Types`, and different `Algorithms` within those types. You can set the
default analysis type in the plugin settings.
There is also the option to hide `Infinity` and `Zero` values.
## Documentation on Algorithms
You can read more about the implemented algorithms, or let us know which you
want us to add, over
[here](https://neo4j.com/docs/graph-data-science/current/algorithms/) 👀.
Information on co-citations can mostly be found on
[Wikipedia](https://en.wikipedia.org/wiki/Co-citation). In particular, we
implement a variation of
[Co-citatition Proximity Analysis](https://en.wikipedia.org/wiki/Co-citation_Proximity_Analysis).
## Buy Us a Coffee
SkepticMystic: [](https://ko-fi.com/G2G454TZF)
Emile: [](https://ko-fi.com/Emile)