Can anyone please let me know if there are any graph based reinforcement learning environments (like networkx for normal graph problems). My aim is to develop a reinforcement learning environment for solving graph based problems like greedy graph coloring.
Related
In the video on why transformers are better https://youtu.be/udY0GlYXXbE?t=380 -
what is the exact benefit of being able to model as a graph? It is somehow not clear to me.
Can we not represent CNN with a graph?
I am trying to rewrite the code of this paper: https://doi.org/10.1038/s42003-020-0837-0
I have written the code step-by-step based on the instructions mentioned in the methods section. But after clustering, for plotting the clusters by dimplot, I receive a dissimilar plot compared to the same plot in the paper.
I wonder what is the problem? I have tailored every parameter to receive the same plot but it hasn't worked yet.
Graph of the paper
My graph
Please help me to solve this issue.
I am starting a new project in python (to be used through jupyter-notebooks), where I'll need to visualise some hierarchically clustered graphs.
I have looked for existing packages, but so far I am not convinced by what I have seen.
I am not interested in the clustering process in itself, because this will be another part of the project and I know (roughly) how the graphs will be built up progressivelly.
What I am looking for are:
an appropriate data structure for storing hierarchically clustered graphs,
visualisation tools that would allow to represent the graph on a map (based on X and Y coordinates of the nodes) and either represent the subparts of the clusters, or simplify the clusters depending on their type or depth in the graph structure,
ideally, bring some interactivity, for example the ability to zoom-in or-out, or click on some clustered nodes to expand the nodes that were hidden in the cluster.
It looks pretty specific and despite some cool packages I have seen I am not sure which one would help without having too much to reimplement. So far, NetworkX looks like a cool starting point, especially with some D3.js (as shown here), but it is still far from what I have in mind.
Any advice about where to start digging?
Thanks a lot.
Gautier
For Python, Seaborn's clustermaps are nice. Seaborn is mainly meant to be used with Pandas dataframes; however, the documentation for clustermap says it can be rectangular data, and so I think it means other arrays will wor.
See also:
Dendrogram with heat map
SciPy Hierarchical Clustering and Dendrogram Tutorial
Hierarchical Clustering in Python
attached you see a decision boundary graph of a classification algorithm made in ggplot(from this great article)I was curious about if it is possible to replicate such graphs in highcharter, or more specifically: Is it possible to create custom boundary lines / areas for different classification algorithms? I would highly appreciate an example and thank you for your support.
I'm looking for an algorithm to automatically visualise a large DAG. It needs to scale well to hundreds or even thousands of nodes and connections (without turning unreadable). Connections should avoid crossing over each other where possible, and should especially avoid crossing over nodes that they aren't connected to.
Is there any standard algorithm I can adapt for this purpose?
You could check out the scalable force-directed placement algorithm. Graphviz implements this, so if you'd like to preview it before implementing, create a Graphviz file and run sfdp my_dag.gv (or fdp which might be easier to implement).
If that doesn't work for you, you might want something like Circos or Hive Plots. Hive Plots work really well for thousands of nodes for both directed and undirected graphs. The algorithm is described at a high level on the homepage, but there's an accompanying journal article too.
You can try Gephi a graph viz software.
You can feed it with different file type (.gexf, .gdf).
As this is a open source software, you can look inside spatialization algorithms.
url: http://gephi.org/