Use community detection for seperating out protein communities, genome communities

[cjprybol]

https://www.youtube.com/watch?v=F4RVBAGJcFY

https://juliagraphs.org/Graphs.jl/dev/centrality/#Graphs.betweenness_centrality

Modularity is a measure of the structure of networks or graphs which measures the strength of division of a network into modules (also called groups, clusters or communities). Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules. Modularity is often used in optimization methods for detecting community structure in networks. However, it has been shown that modularity suffers a resolution limit and, therefore, it is unable to detect small communities. Biological networks, including animal brains, exhibit a high degree of modularity.

https://juliagraphs.org/Graphs.jl/dev/community/#Graphs.modularity-Tuple{AbstractGraph,%20AbstractVector{%3C:Integer}}

https://en.wikipedia.org/wiki/Louvain_method

[cjprybol]

https://en.wikipedia.org/wiki/Girvan%E2%80%93Newman_algorithm

[cjprybol]

overlapping k-means clustering and other clusters that allow overlaps

[cjprybol]

graph partitioning

[cjprybol]

kernighan lin (local) spectral partitioning (global)

[cjprybol]

https://www.youtube.com/watch?v=SWYuUwr1gvA

[cjprybol]

if we do the girvan newman algorithm, we should watch to see when the # of connected components starts to plateau, and then use that. Want to do the minimal # of cuts

May want to weight the degreeness_centrality by the weight of the edge, e.g. high cut likelihood & low weight = cut

[cjprybol]

I may come back to this later, but as of right now I think coverage based thresholding and error correction, plus longer k-lengths for graph simplification, negate most of the need for these