By distance, I assume that you mean "shortest path".

There are a lot of software solutions to this problem. For any kind of network analysis, I recommend the igraph package. It can be used from R, Python, Ruby or C. As an example, here is how you would read a network into R:

library(igraph)
# assume network is a file in GML format
g <- read.graph("myfile.gml", format = "gml")

Then consult the R documentation for functions related to "path". For example:

shortest.paths(g)

will return a matrix of N x N (where N is the number of vertices), containing the path lengths.

For a more visual approach, either Cytoscape or Gephi may be useful. In Gephi, for example, you can import a network file, select the "shortest path" tool from the menu and then click on 2 vertices to see the path length.

• http://www.geneinfinity.org/sp/sp_proteininteraction.html
• http://arxiv.org/pdf/q-bio/0405028
• Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners
• Similarity of phylogenetic trees as indicator of protein–protein interaction
• Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices.

You can use cytoscape PPI network analysis

http://www.cytoscape.org/

Here is short tutorial on Protein interaction network analysis using Cytoscape

http://www.doc.ic.ac.uk/~natasha/course/docs/cytoscapeTutorial.pdf

For implementing cluster algorithm, you can install clusterMaker plugin in Cytoscape

http://www.cgl.ucsf.edu/cytoscape/cluster/clusterMaker.html