\cite{nr}, where nodes are species in an environment, edges represent dependence of one species on another, and the weight is the amount of dependence of one species on another.

Random Networks 

Random networks were built using a Poisson random distribution that determined both the degree of each node, and the weight of each is defined as the inverse of the Euclidean distance between two nodes in a Delaunay triangulation layout. 

Metrics Used

To evaluate the networks, and note the differences the networks, a suite of metrics were used. We will go through each metric, and the reasoning for using the metric.
Betweenness:
    Betweenness allows for us to measure the bottlenecking factor of each node by measuring the number of shortest paths between two pairs of vertices 

Results

To form a baseline on how our metrics would perform, we ran the suite of metrics on 
\cite{nr}
\cite{nr}

Limitations of This Work

Conclusion

\cite{altacs2013analyzing}
\cite{dunn2006novel}
\cite{sporns2003graph}
\cite{xu2002neural}
\cite{de2014laplacian}
\cite{zhang2017network}
\cite{zhou2014object}
\cite{lettvin1959frog}
\cite{guss2018characterizing}
\cite{papo2015network}
\cite{golden1996mathematical}
\cite{michel1989qualitative}
\cite{pearson2013visualizing}
\cite{stam2007graph}
\cite{wiatowski2018mathematical}
\cite{pellionisz1987tensor}
\cite{pellionisz1988tensorial}
\cite{pellionisz1980tensorial}
\cite{pellionisz1985tensor}
\cite{grassi2010centrality}
\cite{lee2010role}
\cite{borgatti2005centrality}
\cite{breza2015network}
\cite{belau2014consequences}
\cite{del:Pozo:2011}
\cite{batool2014towards}
\cite{joyce2010new}
\cite{grassi2010centrality}

Future Work

Constant edge number, The nature of skip connections in layered systems is an interesting one,

Appendix A 

Edmonds-Karp Algorithm for Max Flow