Basic Usage

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This vignette shows how to cluster the vertices of a weighted graph using the random walk method developed by Harel and Koren [1] with the Rwclust package using an example.



The random walk algorithm is based on the concepts of connected components and cut edges. Let $G=(V, E)$ be a graph where $V$ is the set of vertices and $E$ is the set of edges. A connected component is a subset of vertices that are mutually reachable. A graph can have one connected component constituting the entire graph, or several connected components. If a graph has more than one connected component, the graph is said to be disconnected. In this context, the connected components correspond to clusters.

A set of cut edges is a set $E' \subset E$ such that $G-E$ is disconnected. Essentially, $E'$ contains a set of edges whose removal results in the creation of separate clusters of vertices.

The random walk algorithm finds a set of cut edges but "sharpening" the difference between the weights of edges which connect vertices in within a cluster and the weights of edges that run between clusters. All edges with weights below a certain user-defined threshold are deleted and the resulting connected components become the clusters.

The high-level steps in the algorithm are:

  1. sharpen edge weights using random walks
  2. select a cutoff using a histogram of the weights
  3. delete edges below the cutoff and compute the connected components of the resulting graph

Usage Example

We will use an example graph taken [1]. The data is contained in a dataframe. We use igraph to create a graph object a display it along with the edge weights.

data(example1, package="Rwclust")
labels <- c(1,1,1,1,2,2,2,2,3,3,3,3,3,3,3,3,3,4,4,4,4)

G <- igraph::graph_from_edgelist(as.matrix(example1[, c(1,2)]), directed=FALSE)
G <- igraph::set_edge_attr(G, "weight", value=example1$weight)

plot(G, edge.label=E(G)$weight, vertex.color=labels, layout=layout_with_fr)

1. Sharpening Edge Weights

The rwclust function applies the edge-sharpening algorithm and returns a list containing a vector of the final weights and a sparse matrix representing the weighted adjacency matrix of the new graph. The iter parameter is the number of algorithm iterations and k is the maximum length of random walk to consider.

result <- rwclust(example1, iter=6, k=3)

Next we plot the new weights. Notice how the weights connecting the clusters are 0 and all the other weights are larger than their original values.

G_sharpened <- igraph::graph_from_edgelist(as.matrix(example1[, c(1,2)]), directed=FALSE)
E(G_sharpened)$weights <- round(weights(result),0)
plot(G_sharpened, edge.label=E(G_sharpened)$weights, vertex.color=labels, layout=layout_with_fr)

2. Plot the Histogram

Before edges are deleted and the connected components calculated, the user must select a cutoff. To do this we plot a histogram of the edge weights. Note that there appear to be several edges with very small edge weights. 25 seems to be an appropriate cutoff.

plot(result, cutoff=25, breaks=20)

3. Delete Edges and Compute Components

The next step is to remove the edges that are below the threshold and compute the connected components.

# delete edges with weights below the threshold
edges_to_keep <- which(weights(result) > 25)
example1_c <- example1[edges_to_keep, ]
example1_c$weight <- weights(result)[edges_to_keep]

G_c <- igraph::graph_from_edgelist(as.matrix(example1_c[, c(1,2)]), directed=FALSE)

# compute the connected components
clusters <- igraph::components(G_c)$membership

plot(G_c, vertex.color=clusters)


Harel, David, and Yehuda Koren. "On clustering using random walks." International Conference on Foundations of Software Technology and Theoretical Computer Science. Springer, Berlin, Heidelberg, 2001.

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Rwclust documentation built on July 25, 2022, 1:05 a.m.