Identifying Communities and Key Vertices by Reconstructing Networks from Samples
arXiv:1304.6554 · doi:10.1371/journal.pone.0061006
Abstract
Sampling techniques such as Respondent-Driven Sampling (RDS) are widely used in epidemiology to sample "hidden" populations, such that properties of the network can be deduced from the sample. We consider how similar techniques can be designed that allow the discovery of the structure, especially the community structure, of networks. Our method involves collecting samples of a network by random walks and reconstructing the network by probabilistically coalescing vertices, using vertex attributes to determine the probabilities. Even though our method can only approximately reconstruct a part of the original network, it can recover its community structure relatively well. Moreover, it can find the key vertices which, when immunized, can effectively reduce the spread of an infection through the original network.
15 pages, 17 figures
References in corpus (8)
- Assortative mixing in networks
- Maps of random walks on complex networks reveal community structure
- Benchmark graphs for testing community detection algorithms
- Link Prediction in Complex Networks: A Survey
- Self-similar community structure in organisations
- Graph Evolution: Densification and Shrinking Diameters
- Characterizing the community structure of complex networks
- Predicting link directions via a recursive subgraph-based ranking