Title |
Identifying Communities and Key Vertices by Reconstructing Networks from Samples
|
---|---|
Published in |
PLOS ONE, April 2013
|
DOI | 10.1371/journal.pone.0061006 |
Pubmed ID | |
Authors |
Bowen Yan, Steve Gregory |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Philippines | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 4% |
Lebanon | 1 | 4% |
United States | 1 | 4% |
Unknown | 23 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 23% |
Researcher | 4 | 15% |
Lecturer > Senior Lecturer | 3 | 12% |
Student > Bachelor | 2 | 8% |
Other | 2 | 8% |
Other | 8 | 31% |
Unknown | 1 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 6 | 23% |
Medicine and Dentistry | 5 | 19% |
Social Sciences | 4 | 15% |
Agricultural and Biological Sciences | 3 | 12% |
Business, Management and Accounting | 2 | 8% |
Other | 3 | 12% |
Unknown | 3 | 12% |