# Build Watts-Strogatz Small World Graph Model

This example shows how to construct and analyze a Watts-Strogatz small-world graph. The Watts-Strogatz model is a random graph that has small-world network properties, such as clustering and short average path length.

### Algorithm Description

Creating a Watts-Strogatz graph has two basic steps:

Create a ring lattice with nodes of mean degree . Each node is connected to its nearest neighbors on either side.

For each edge in the graph, rewire the target node with probability . The rewired edge cannot be a duplicate or self-loop.

After the first step the graph is a perfect ring lattice. So when , no edges are rewired and the model returns a ring lattice. In contrast, when , all of the edges are rewired and the ring lattice is transformed into a random graph.

The file `WattsStrogatz.m`

implements this graph algorithm for undirected graphs. The input parameters are `N`

, `K`

, and `beta`

according to the algorithm description above.

View the file `WattsStrogatz.m`

.

% Copyright 2015 The MathWorks, Inc. function h = WattsStrogatz(N,K,beta) % H = WattsStrogatz(N,K,beta) returns a Watts-Strogatz model graph with N % nodes, N*K edges, mean node degree 2*K, and rewiring probability beta. % % beta = 0 is a ring lattice, and beta = 1 is a random graph. % Connect each node to its K next and previous neighbors. This constructs % indices for a ring lattice. s = repelem((1:N)',1,K); t = s + repmat(1:K,N,1); t = mod(t-1,N)+1; % Rewire the target node of each edge with probability beta for source=1:N switchEdge = rand(K, 1) < beta; newTargets = rand(N, 1); newTargets(source) = 0; newTargets(s(t==source)) = 0; newTargets(t(source, ~switchEdge)) = 0; [~, ind] = sort(newTargets, 'descend'); t(source, switchEdge) = ind(1:nnz(switchEdge)); end h = graph(s,t); end

### Ring Lattice

Construct a ring lattice with 500 nodes using the `WattsStrogatz`

function. When `beta`

is 0, the function returns a ring lattice whose nodes all have degree `2K`

.

h = WattsStrogatz(500,25,0); plot(h,'NodeColor','k','Layout','circle'); title('Watts-Strogatz Graph with $N = 500$ nodes, $K = 25$, and $\beta = 0$', ... 'Interpreter','latex')

### Some Random Edges

Increase the amount of randomness in the graph by raising `beta`

to `0.15`

and `0.50`

.

h2 = WattsStrogatz(500,25,0.15); plot(h2,'NodeColor','k','EdgeAlpha',0.1); title('Watts-Strogatz Graph with $N = 500$ nodes, $K = 25$, and $\beta = 0.15$', ... 'Interpreter','latex')

h3 = WattsStrogatz(500,25,0.50); plot(h3,'NodeColor','k','EdgeAlpha',0.1); title('Watts-Strogatz Graph with $N = 500$ nodes, $K = 25$, and $\beta = 0.50$', ... 'Interpreter','latex')

### Random Graph

Generate a completely random graph by increasing `beta`

to its maximum value of `1.0`

. This rewires all of the edges.

h4 = WattsStrogatz(500,25,1); plot(h4,'NodeColor','k','EdgeAlpha',0.1); title('Watts-Strogatz Graph with $N = 500$ nodes, $K = 25$, and $\beta = 1$', ... 'Interpreter','latex')

### Degree Distribution

The degree distribution of the nodes in the different Watts-Strogatz graphs varies. When `beta`

is 0, the nodes all have the same degree, `2K`

, so the degree distribution is just a Dirac-delta function centered on `2K`

, . However, as `beta`

increases, the degree distribution changes.

This plot shows the degree distributions for the nonzero values of `beta`

.

histogram(degree(h2),'BinMethod','integers','FaceAlpha',0.9); hold on histogram(degree(h3),'BinMethod','integers','FaceAlpha',0.9); histogram(degree(h4),'BinMethod','integers','FaceAlpha',0.8); hold off title('Node degree distributions for Watts-Strogatz Model Graphs') xlabel('Degree of node') ylabel('Number of nodes') legend('\beta = 1.0','\beta = 0.50','\beta = 0.15','Location','NorthWest')

### Hub Formation

The Watts-Strogatz graph has a high clustering coefficient, so the nodes tend to form cliques, or small groups of closely interconnected nodes. As `beta`

increases towards its maximum value of `1.0`

, you see an increasingly large number of hub nodes, or nodes of high relative degree. The hubs are a common connection between other nodes and between cliques in the graph. The existence of hubs is what permits the formation of cliques while preserving a short average path length.

Calculate the average path length and number of hub nodes for each value of `beta`

. For the purposes of this example, the hub nodes are nodes with degree greater than or equal to 55. These are all of the nodes whose degree increased 10% or more compared to the original ring lattice.

n = 55; d = [mean(mean(distances(h))), nnz(degree(h)>=n); ... mean(mean(distances(h2))), nnz(degree(h2)>=n); ... mean(mean(distances(h3))), nnz(degree(h3)>=n); mean(mean(distances(h4))), nnz(degree(h4)>=n)]; T = table([0 0.15 0.50 1]', d(:,1), d(:,2),... 'VariableNames',{'Beta','AvgPathLength','NumberOfHubs'})

T = 4x3 table Beta AvgPathLength NumberOfHubs ____ _____________ ____________ 0 5.48 0 0.15 2.0715 20 0.5 1.9101 85 1 1.9008 92

As `beta`

increases, the average path length in the graph quickly falls to its limiting value. This is due to the formation of the highly connected hub nodes, which become more numerous as `beta`

increases.

Plot the Watts-Strogatz model graph, making the size and color of each node proportional to its degree. This is an effective way to visualize the formation of hubs.

colormap hsv deg = degree(h2); nSizes = 2*sqrt(deg-min(deg)+0.2); nColors = deg; plot(h2,'MarkerSize',nSizes,'NodeCData',nColors,'EdgeAlpha',0.1) title('Watts-Strogatz Graph with $N = 500$ nodes, $K = 25$, and $\beta = 0.15$', ... 'Interpreter','latex') colorbar