In `Gmatch4py`, algorithms manipulate `networkx.Graph`, a complete graph model that
In `GMatch4py`, algorithms manipulate `networkx.Graph`, a complete graph model that
comes with a large spectrum of parser to load your graph from various inputs : `*.graphml,*.gexf,..` (check [here](https://networkx.github.io/documentation/stable/reference/readwrite/index.html) to see all the format accepted)
### Use Gmatch4py
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@@ -61,8 +57,8 @@ print(result)
The output is a similarity/distance matrix :
```
Out[10]:
array([[0., 7.],
[7., 0.]])
array([[0., 14.],
[10., 0.]])
```
This output result is "raw", if you wish to have normalized results in terms of distance (or similarity) you can use :
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@@ -77,8 +73,9 @@ ged.distance(result)
## List of algorithms
* DeltaCon and DeltaCon0 (*debug needed*) [1]
* Vertex Ranking (*debug needed*) [2]
* Vertex Ranking [2]
* Vertex Edge Overlap [2]
* Bag of Nodes (a bag of words model using nodes as vocabulary)
* Bag of Cliques (a bag of words model using cliques as vocabulary)
* Graph kernels
* Random Walk Kernel (*debug needed*) [3]
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@@ -109,15 +106,10 @@ ged.distance(result)
Jacques Fize, *jacques[dot]fize[at]cirad[dot]fr*
Some algorithms coming from other projects were integrated to Gmatch4py. **Be assured that
Some algorithms from other projects were integrated to Gmatch4py. **Be assured that
each code is associated with a reference to the original.**
## TODO List
* Debug algorithms --> :runner:
* Improve code structure and performance :runner:
* Simplify `setup.py` :heavy_check_mark:
* Some algorithms are distance and others are similarity measure. Must change the compare
methods so it can adapt to the user need. For example, maybe the user want to deal with
graph similarity rather than distance between graph. :heavy_check_mark: