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Clean README, notebook and docs (#141)
* Fix a typo and convert LaTex to code inline for readability * Fix a typo. * Clean README, notebook and docs
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CONTRIBUTING.md

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@@ -15,7 +15,7 @@ Please include version numbers of all relevant libraries and Julia itself.
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- Correctness is a necessary requirement; efficiency is desirable. Once you have a correct implementation, make a Pull Request (PR) so we can help improve performance.
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- PRs should contain one logical enhancement to the codebase.
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- Squash commits in a PR.
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- If you want to introduce a new feature, open an issue can to discuss a feature before you start coding (this maximizes the likelihood of patch acceptance).
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- If you want to introduce a new feature, open an issue to discuss a feature before you start coding (this maximizes the likelihood of patch acceptance).
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- Minimize dependencies on external packages, and avoid introducing new dependencies that would increase the compilation time by a lot.
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- Put type assertions on all function arguments where conflict may arise (use abstract types, `Union`, or `Any` if necessary).
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- If the algorithm was presented in a paper, include a reference to the paper (_e.g._, a proper academic citation along with an eprint link).

README.md

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@@ -20,7 +20,7 @@ pkg> add Graphs
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## Basic use
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_Graphs.jl_ includes numerous convenience functions for generating functions, such as `path_graph`, which builds a simple undirected [path graph](https://en.wikipedia.org/wiki/Path_graph) of a given length. Once created, these graphs can be easily interrogated and modified.
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_Graphs.jl_ includes numerous convenience functions for generating graphs, such as `path_graph`, which builds a simple undirected [path graph](https://en.wikipedia.org/wiki/Path_graph) of a given length. Once created, these graphs can be easily interrogated and modified.
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```julia-repl
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julia> g = path_graph(6)

docs/notebooks/triangles.ipynb

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" end\n",
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" return is_directed(g) ? (c , k*(k-1)) : (div(c,2) , div(k*(k-1),2))\n",
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"end\n",
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"\n",
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"function local_clustering(g::AbstractGraph, vs = vertices(g))\n",
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" ntriang = zeros(Int, length(vs))\n",
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" nalltriang = zeros(Int, length(vs))\n",
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" ntriang[i], nalltriang[i] = local_clustering(g, v)\n",
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" end\n",
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" return ntriang, nalltriang\n",
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"end\n"
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"end"
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]
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},
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{
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" \n",
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" return is_directed(g) ? (tcount , k*(k-1)) : (div(tcount,2) , div(k*(k-1),2))\n",
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"end\n",
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"\n",
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"function local_clustering!(storage::AbstractVector{Bool}, g::AbstractGraph, vs = vertices(g))\n",
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" ntriang = zeros(Int, length(vs))\n",
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" nalltriang = zeros(Int, length(vs))\n",
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"\n",
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"It looks like the bool array is slightly faster even though we have to reset it. Let's use that."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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