Bootstrapping network partitioning methods

Sat 17 April 2010

My PhD research at the moment focuses on network-based algorithms for delineating functional regions (geographical regions within which a large majority of the local population seeks employment, and the majority of local employers recruit their labour). Currently I’m using a network partitioning algorithm based on modularity maximisation. I have found my results to be quite good so far, but, ‘quite good’ isn’t really a very scientific description of validity, so obviously some others means of validation is required. Enter bootstrap resampling!

Bootstrapping can be used to assess the validity of a particular network partitioning by measuring the stability of the detected partitions (or clusters). Here, a cluster may be thought of as stable if, for example, it remains relatively invariant to random- or sampling-error and noise. In this sense, we’re interested in distinguishing between clusters which reflect the true nature of the dataset, and those generated as a result of random effects, data uncertainties, or measurement error.

The process works like this:

  1. Generate a large number of random ‘bootstrap samples’ from a (directed) weighted network,
  2. Apply some network partitioning algorithm to the original network,
  3. (Re)apply the network partitioning algorithm to each bootstrap sample,
  4. For each cluster in the original network partitioning, the most similar cluster in each bootstrap replicate is found using the Jaccard coeffcient γ as a measure of similarity, and similarity is recorded,
  5. The stability of each cluster is assessed based on the mean Jaccard similarity over all resampled datasets.

Once the above process is run, we get an estimate of how stable each cluster is. We can then use this information to decide which clusters to keep, and which ones need to be merged with their closest neighbour. There are several ways to specify how we resample the data. If we assume no specific structure in the dataset, regular non-parametric bootstrap resampling will work fine, however, alternative resampling strategies include: a) replacing network edge weights with noise, b) adding a small amount of noise to (a percentage of) the network edges, or c) using only a subset of the original network (i.e., generating a subgraph of the original network).

I tested this process on a computer generated network with three predefined clusters using resampling strategy (b) above, by adding random noise to k percent of the network edges, and observed the effect of increasing levels of uncertainty by applying the resampling technique to increasing values of k. The results show just what we would expect: as more noise is added to the dataset, the stability of the detected clusters goes down. The nice bit however, is that for k <= 0.5, the detected clusters remained relatively stable (γ >= 0.6), meaning the network partitioning algorithm I was using is doing a pretty good job. Nice!

This bootstrapping process is part of a paper I’m working on at the moment, and uses a geographical variant of this algorithm to detect functional regions in travel to work data. I’ll post more on the algorithm and my bootstrapping implementation in R (using the very cool foreach package) here soon.


Leicht, E. A., & Newman, M. E. J.(2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.

Hennig, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics & Data Analysis, 52(1), 258-271.


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