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A Ruby command-line utility to generate Watts-Strogatz model networks
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README.md

Watts-Strogatz model network generator

A Ruby command-line utility to generate Watts-Strogatz model networks defining input parameters (number of nodes, beta parameter) and collecting output parameters (average path length, the clustering coefficient).

Usage

Require Ruby 1.9+.

Run ruby bin/wsmodel [options]

Options are:

-n, --nodes n                    Number of nodes (default: 1000)
-d, --degree d                   Node degree (default: 10)
-i, --iterations i               Number of iterations (default: 20)

It performs the experiment of the Collective dynamics of 'small-world' network article (Figure 2) and outputs the resulting average path lengths and clustering coefficients.

Running tests

Run ruby spec/suite.rb

Terminology

Various concepts can be named by different equivalent words in the various sources mentioned here.

  • node = vertice
  • link = edge, connection
  • node degree k = number of links per node
  • parameter beta = probability p
  • characteristic path length = average path length

Background

Main Sources:

Watts and Strogatz published their network model in 1998. They intended to create the simplest model that can produce graphs with small-world properties.

Small-worlds network are graphs in which most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps.

It is translated in graph theory by two main properties:

  1. a large clustering coefficient: the nodes in the graph tends to cluster together
  2. a low average path length: two nodes can be joined in a few steps

A classical example of small-world network is the graph of human relationships, where two complete strangers can be linked through a chain of few acquaintances. There are many other examples of small-world network.

The Watts-Strogatz model network construction

It is built on a ring lattice, basically it means that the network nodes are arranged along a circle. It is a useful topology because it allows to start from a clean ordered network, where initially each node is connected to its neighbours.

From this initial lattice the network is built by injecting some tunable randomness in the node connections. For that the links are randomly rewired.

How? Each link in the lattice is visited and is randomly rewired with some probability. This probability is the parameter beta. For instance a link from node A to B has a beta probability to be rewired from A to another random node Bnew in the lattice.

Construction of a Watts-Strogratz model network

When beta is 0 the lattice remains unchanged. When beta is 1 all links are rewired, generating a full random network. In the middle the network is partly ordered and partly random.

Here is the exact rewiring algorithm described in Collective dynamics of 'small-world' network, legend of figure 1:

Note first that:

  • vertex = node
  • edge = link
  • probability p = probability beta

"We choose a vertex and the edge that connects it to its nearest neighbour in a clockwise sense. With probability p, we reconnect this edge to a vertex chosen uniformly at random over the entire ring, with duplicate edges forbidden; otherwise we leave the edge in place. We repeat this process by moving clockwise around the ring, considering each vertex in turn until one lap is completed. Next, we consider the edges that connect vertices to their second-nearest neighbours clockwise. As before, we randomly rewire each of these edges with probability p, and continue this process, circulating around the ring and proceeding outward to more distant neighbours after each lap, until each edge in the original lattice has been considered once. (As there are nk/2 edges in the entire graph, the rewiring process stops after k/2 laps.)"

Interest of the Watts-Strogatz model

Networks generated by this simple model exhibit small-world properties, i.e. a large clustering coefficient and a low average path length.

We can calculate those values based on the possible values of the input parameter beta, and thus see which range of beta values produces small-world networks.

The model shows also that small-world networks can arise from a very simple compromise between very basic forces - order & disorder - and not from the specific mechanisms by which that compromise brokered (the link rewiring in the model is a random process, it does not follow some rules).

Exact results obtained by Watts and Strogatz

They will serve as the reference to check the correctness and appropriate use of the my implementation.

The values of the clustering coefficient and the average path length based on the beta parameter are presented and detailed on the figure 2 of the article Collective dynamics of 'small-world' network.

Calculation of the clustering coefficient

From the "clustering coefficient" Wikipedia page.

We want to calculate the "Network average clustering coefficient". It is the average of the local clustering coefficients of all the nodes.

The local clustering coefficient of a node is the proportion of links between the nodes within its neighbourhood divided by the number of links that could possibly exist between them (the neighbourhoud of a node is the set of all the nodes linked to this node).

We can say that the local clustering coefficient of a node is the interconnectedness of its neighbours. See an example with the public transport network of Turkey

From Wikipedia:

Local clustering coefficient of the blue node in different graphs

"The local clustering coefficient of the light blue node is computed as the proportion of connections among its neighbors which are actually realized compared with the number of all possible connections. In the figure, the light blue node has three neighbours, which can have a maximum of 3 connections among them. In the top part of the figure all three possible connections are realised (thick black segments), giving a local clustering coefficient of 1. In the middle part of the figure only one connection is realised (thick black line) and 2 connections are missing (dotted red lines), giving a local cluster coefficient of 1/3. Finally, none of the possible connections among the neighbours of the light blue node are realised, producing a local clustering coefficient value of 0."

Calculation of the average path length

From the "average path length" Wikipedia page.

It is defined as the average number of steps along the shortest paths for all possible pairs of network nodes. It is a measure of the efficiency of information or mass transport on a network.

Basically the algorithm is:

  1. for each possible pair of nodes, calculate the shortest path length between them
  2. calculate the average of those shortest paths

To calculate the shortest path between two nodes A and B, we can use a Breadth-first search where the starting node is A and the goal node is B. As the search is done through the successive "layers" of neighbours of node A, the first time we found the node B we know we found it through the shortest possible path.

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