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% Document Info
\author{RJ Murray (murenei)}
\pdfinfo{
  /Title (network-analysis-with-python-and-networkx.pdf)
  /Creator (Cheatography)
  /Author (RJ Murray (murenei))
  /Subject (Network Analysis with Python and NetworkX Cheat Sheet)
}

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\fancyhead[L]{
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
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    \vspace{-7pt}
    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\columnbreak
\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Network Analysis with Python and NetworkX Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{RJ Murray (murenei)} via \textcolor{DarkBackground}{\uline{cheatography.com/58736/cs/15946/}}}
\end{tabulary}
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\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
  \SetRowColor{FootBackground}
  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}RJ Murray (murenei) \\
  \uline{cheatography.com/murenei} \\
        \uline{\seqsplit{tutify}.com.au}
  \end{tabulary}
\vfill
\columnbreak
\begin{tabulary}{5.8cm}{L}
  \SetRowColor{FootBackground}
  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 4th June, 2018.\\
   Updated 4th June, 2018.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
\vfill
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Sponsor}}  \\
  \SetRowColor{white}
  \vspace{-5pt}
  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
\end{tabulary}
\end{multicols}}




\begin{document}
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\begin{multicols*}{2}

\begin{tabularx}{8.4cm}{x{4 cm} x{4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Basic graph manipulation}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{8.4cm}}{`import networkx as nx`} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{8.4cm}}{`G=nx.Graph()`} \tn 
% Row Count 2 (+ 1)
% Row 2
\SetRowColor{LightBackground}
`G=nx.MultiGraph()` & Create a graph allowing parallel edges \tn 
% Row Count 4 (+ 2)
% Row 3
\SetRowColor{white}
`G.add\_edges\_from({[}(0, 1),(0, 2),(1, 3),(2, 4){]}` & Create graph from edges \tn 
% Row Count 7 (+ 3)
% Row 4
\SetRowColor{LightBackground}
\seqsplit{`nx.draw\_networkx(G)`} & Draw the graph \tn 
% Row Count 9 (+ 2)
% Row 5
\SetRowColor{white}
`G.add\_node('A',role='manager')` & Add a node \tn 
% Row Count 11 (+ 2)
% Row 6
\SetRowColor{LightBackground}
`G.add\_edge('A','B',relation = 'friend')` & Add an edge \tn 
% Row Count 14 (+ 3)
% Row 7
\SetRowColor{white}
`G.node{[}'A'{]}{[}'role'{]} = 'team member'` & Set attribute of a node \tn 
% Row Count 16 (+ 2)
% Row 8
\SetRowColor{LightBackground}
`G.node{[}'A'{]}`, `G.edge{[}('A','B'){]}` & View attributes of node, edge \tn 
% Row Count 18 (+ 2)
% Row 9
\SetRowColor{white}
`G.edges()`, `G.nodes()` & Show edges, nodes \tn 
% Row Count 20 (+ 2)
% Row 10
\SetRowColor{LightBackground}
`list(G.edges())` & Return as list instead of EdgeView class \tn 
% Row Count 22 (+ 2)
% Row 11
\SetRowColor{white}
\seqsplit{`G.nodes(data=True)`}, \seqsplit{`G.edges(data=True)`} & Include node/edge attributes \tn 
% Row Count 25 (+ 3)
% Row 12
\SetRowColor{LightBackground}
\seqsplit{`G.nodes(data='relation)`} & Return specific attribute \tn 
% Row Count 27 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{4.64 cm} x{3.36 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Creating graphs from data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\seqsplit{`G=nx.read\_adjlist('G\_adjlist.txt'}, nodetype=int)` & Create from adjacency list \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
`G=nx.Graph(G\_mat)` & Create from matrix (np.array) \tn 
% Row Count 5 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{`G=nx.read\_edgelist('G\_edgelist.txt'}, data={[}('Weight', int){]})` & Create from edgelist \tn 
% Row Count 8 (+ 3)
% Row 3
\SetRowColor{white}
\seqsplit{`G=nx.from\_pandas\_dataframe(G\_df}, 'n1', 'n2', edge\_attr='weight')` & Create from df \tn 
% Row Count 11 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{8.4cm}}{Adjacency list format \newline 0 1 2 3 5 \newline 1 3 6 ... \newline  \newline Edgelist format: \newline 0 1 14 \newline 0 2 17}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{4 cm} x{4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Bipartite graphs}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{8.4cm}}{`from networkx.algorithms import bipartite`} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\seqsplit{`bipartite.is\_bipartite(B)`} & Check if graph B is bipartite \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{`bipartite.is\_bipartite\_node\_set(B},set)` & Check if set of nodes is bipartition of graph \tn 
% Row Count 6 (+ 3)
% Row 3
\SetRowColor{white}
`bipartite.sets(B)` & Get each set of nodes of bipartite graph \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\seqsplit{`bipartite.projected\_graph(B}, X)` & Bipartite projected graph - nodes with bipartite friends in common \tn 
% Row Count 12 (+ 4)
% Row 5
\SetRowColor{white}
\seqsplit{`P=bipartite.weighted\_projected\_graph(B}, X)` & projected graph with weights (number of friends in common) \tn 
% Row Count 15 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
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\begin{tabularx}{8.4cm}{x{3.76 cm} x{4.24 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Network Connectivity}}  \tn
% Row 0
\SetRowColor{LightBackground}
`nx.clustering(G, node)` & Local clustering coefficient \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\seqsplit{`nx.average\_clustering(G)`} & Global clustering coefficient \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{`nx.transitivity(G)`} & Transitivity (\% of open triads) \tn 
% Row Count 6 (+ 2)
% Row 3
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\seqsplit{`nx.shortest\_path(G},n1,n2)` & Outputs the path itself \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{8.4cm}}{\seqsplit{`nx.shortest\_path\_length(G},n1,n2)`} \tn 
% Row Count 9 (+ 1)
% Row 5
\SetRowColor{white}
`T=nx.bfs\_tree(G, n1)` & Create breadth-first search tree from node n1 \tn 
% Row Count 12 (+ 3)
% Row 6
\SetRowColor{LightBackground}
\seqsplit{`nx.average\_shortest\_path\_length(G)`} & Average distance between all pairs of nodes \tn 
% Row Count 15 (+ 3)
% Row 7
\SetRowColor{white}
`nx.diameter(G)` & Maximum distance between any pair of nodes \tn 
% Row Count 17 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\seqsplit{`nx.eccentricity(G)`} & Returns each node's distance to furthest node \tn 
% Row Count 20 (+ 3)
% Row 9
\SetRowColor{white}
`nx.radius(G)` & Minimum eccentricity in the graph \tn 
% Row Count 22 (+ 2)
% Row 10
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`nx.periphery(G)` & Set of nodes where \seqsplit{eccentricity=diameter} \tn 
% Row Count 24 (+ 2)
% Row 11
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`nx.center(G)` & Set of nodes where eccentricity=radius \tn 
% Row Count 26 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
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\begin{tabularx}{8.4cm}{x{3.44 cm} x{4.56 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Connectivity: Network Robustness}}  \tn
% Row 0
\SetRowColor{LightBackground}
\seqsplit{`nx.node\_connectivity(G)`} & Min nodes removed to disconnect a network \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\seqsplit{`nx.minimum\_node\_cut()`} & Which nodes? \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{`nx.edge\_connectivity(G)`} & Min edges removed to disconnect a network \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\seqsplit{`nx.minimum\_edge\_cut(G)`} & Which edges? \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\seqsplit{`nx.all\_simple\_paths(G},n1,n2)` & Show all paths between two nodes \tn 
% Row Count 10 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
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\begin{tabularx}{8.4cm}{x{3.76 cm} x{4.24 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Network Connectivity: Connected Components}}  \tn
% Row 0
\SetRowColor{LightBackground}
\seqsplit{`nx.is\_connected(G)`} & Is there a path between every pair of nodes? \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
\seqsplit{`nx.number\_connected\_components(G)`} & \# separate components \tn 
% Row Count 5 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{`nx.node\_connected\_component(G}, N)` & Which connected component does {\emph{N}} belong to? \tn 
% Row Count 8 (+ 3)
% Row 3
\SetRowColor{white}
\seqsplit{`nx.is\_strongly\_connected(G)`} & Is the network connected directionally? \tn 
% Row Count 10 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\seqsplit{`nx.is\_weakly\_connected(G)`} & Is the directed network connected if assumed undirected? \tn 
% Row Count 13 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
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\begin{tabularx}{8.4cm}{x{3.68 cm} x{4.32 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Common Graphs}}  \tn
% Row 0
\SetRowColor{LightBackground}
\seqsplit{`G=nx.karate\_club\_graph()`} & Karate club graph (social network) \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\seqsplit{`G=nx.path\_graph(n)`} & Path graph with n nodes \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{`G=nx.complete\_graph(n)`} & Complete graph on n nodes \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\seqsplit{`G=random\_regular\_graph(d},n)` & Random d-regular graph on n-nodes \tn 
% Row Count 8 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{8.4cm}}{See \{\{popup="http://https://networkx.github.io/documentation/networkx-1.10/reference/generators.html"\}\}NetworkX Graph Generators reference\{\{/link\}\} for more. \newline Also see "An Atlas of Graphs" by Read and Wilson (1998).}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{4 cm} x{4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Influence Measures and Network Centralization}}  \tn
% Row 0
\SetRowColor{LightBackground}
\seqsplit{`dc=nx.degree\_centrality(G)`} & Degree centrality for network \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
`dc{[}node{]}` & Degree centrality for a node \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{`nx.in\_degree\_centrality(G)`}, \seqsplit{`nx.out\_degree\_centrality(G)`} & DC for directed networks \tn 
% Row Count 7 (+ 3)
% Row 3
\SetRowColor{white}
\seqsplit{`cc=nx.closeness\_centrality(G},normalized=True)` & Closeness centrality (normalised) for the network \tn 
% Row Count 10 (+ 3)
% Row 4
\SetRowColor{LightBackground}
`cc{[}node{]}` & Closeness centrality for an individual node \tn 
% Row Count 13 (+ 3)
% Row 5
\SetRowColor{white}
\seqsplit{`bC=nx.betweenness\_centrality(G)`} & Betweenness centrality \tn 
% Row Count 15 (+ 2)
% Row 6
\SetRowColor{LightBackground}
`..., normalized=True,...)` & Normalized betweenness centrality \tn 
% Row Count 17 (+ 2)
% Row 7
\SetRowColor{white}
`..., endpoints=False, ...)` & BC excluding endpoints \tn 
% Row Count 19 (+ 2)
% Row 8
\SetRowColor{LightBackground}
`..., K=10,...)` & BC approximated using random sample of K nodes \tn 
% Row Count 22 (+ 3)
% Row 9
\SetRowColor{white}
\seqsplit{`nx.betweenness\_centrality\_subset(G},\{subset\})` & BC calculated on subset \tn 
% Row Count 25 (+ 3)
% Row 10
\SetRowColor{LightBackground}
\seqsplit{`nx.edge\_betweenness\_centrality(G)`} & BC on edges \tn 
% Row Count 27 (+ 2)
% Row 11
\SetRowColor{white}
\seqsplit{`nx.edge\_betweenness\_centrality\_subset(G},\{subset\})` & BC on subset of edges \tn 
% Row Count 30 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{8.4cm}}{Normalization: Divide by number of pairs of nodes.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{4 cm} x{4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{PageRank and Hubs \& Authorities Algorithms}}  \tn
% Row 0
\SetRowColor{LightBackground}
`nx.pagerank(G, alpha=0.8)` & Scaled PageRank of G with dampening parameter \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
`h,a=nx.hits(G)` & HITS algorithm - outputs 2 dictionaries (hubs, authorities) \tn 
% Row Count 6 (+ 3)
% Row 2
\SetRowColor{LightBackground}
`h,a=nx.hits(G,max\_iter=10,normalized=True)` & Constrained HITS and normalized by sum at each stage \tn 
% Row Count 9 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{8.4cm}}{Centrality measures make different assumptions about what it means to be a "central" node. Thus, they produce different rankings.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{4 cm} x{4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Network Evolution - Real-world Applications}}  \tn
% Row 0
\SetRowColor{LightBackground}
`G.degree()`, `G.in\_degree()`, `G.out\_degree()` & Distribution of node degrees \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
Preferential Attachment Model & Results in power law -\textgreater{} many nodes with low degrees; few with high degrees \tn 
% Row Count 7 (+ 4)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{`G=barabasi\_albert\_graph(n},m)` & Preferential Attachment Model with {\emph{n}} nodes and each new node attaching to {\emph{m}} existing nodes \tn 
% Row Count 12 (+ 5)
% Row 3
\SetRowColor{white}
Small World model & High average degree (global clustering) and low average shortest path \tn 
% Row Count 16 (+ 4)
% Row 4
\SetRowColor{LightBackground}
\seqsplit{`G=watts\_strogatz\_graph(n},k,p)` & Small World network of {\emph{n}} nodes, connected to its {\emph{k}} nearest neighbours, with chance {\emph{p}} of rewiring \tn 
% Row Count 22 (+ 6)
% Row 5
\SetRowColor{white}
\seqsplit{`G=connected\_watts\_strogatz\_graph(n},k,p, t)` & {\emph{t}} = max iterations to try to ensure connected graph \tn 
% Row Count 25 (+ 3)
% Row 6
\SetRowColor{LightBackground}
\seqsplit{`G=newman\_watts\_strogatz\_graph(n},k,p)` & {\emph{p}} = probability of adding (not rewiring) \tn 
% Row Count 28 (+ 3)
% Row 7
\SetRowColor{white}
Link Prediction measures & How likely are 2 nodes to connect, given an existing network \tn 
% Row Count 31 (+ 3)
\end{tabularx}
\par\addvspace{1.3em}

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\begin{tabularx}{8.4cm}{x{4 cm} x{4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Network Evolution - Real-world Applications (cont)}}  \tn
% Row 8
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\seqsplit{`nx.common\_neighbors(G},n1,n2)` & Calc common neighbors of nodes {\emph{n1}}, {\emph{n2}} \tn 
% Row Count 3 (+ 3)
% Row 9
\SetRowColor{white}
\seqsplit{`nx.jaccard\_coefficient(G)`} & Normalised common neighbors measure \tn 
% Row Count 5 (+ 2)
% Row 10
\SetRowColor{LightBackground}
\seqsplit{`nx.resource\_allocation\_index(G)`} & Calc RAI of all nodes not already connected by an edge \tn 
% Row Count 8 (+ 3)
% Row 11
\SetRowColor{white}
\seqsplit{`nx.adamic\_adar\_index(G)`} & As per RAI but with log of degree of common neighbor \tn 
% Row Count 11 (+ 3)
% Row 12
\SetRowColor{LightBackground}
\seqsplit{`nx.preferential\_attachment(G)`} & Product of two nodes' degrees \tn 
% Row Count 13 (+ 2)
% Row 13
\SetRowColor{white}
Community Common Neighbors & Common neighbors but with bonus if they belong in same 'community' \tn 
% Row Count 17 (+ 4)
% Row 14
\SetRowColor{LightBackground}
\seqsplit{`nx.cn\_soundarajan\_hopcroft(n1}, n2)` & CCN score for {\emph{n1}}, {\emph{n2}} \tn 
% Row Count 19 (+ 2)
% Row 15
\SetRowColor{white}
`G.node{[}'A'{]}{[}'community'{]}=1` & Add community attribute to node \tn 
% Row Count 21 (+ 2)
% Row 16
\SetRowColor{LightBackground}
\seqsplit{`nx.ra\_index\_soundarajan\_hopcroft(G)`} & Community Resource Allocation score \tn 
% Row Count 23 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{8.4cm}}{These scores give only an indication of whether 2 nodes are likely to connect. \newline To make a link prediction, you would use these scores as features in a classification ML model.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

\end{document}