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X Demographics
Mendeley readers
Attention Score in Context
| 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
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Philippines | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 April 2013.
All research outputs
#19,790,664
of 25,193,883 outputs
Outputs from PLOS ONE
#171,151
of 218,525 outputs
Outputs of similar age
#150,833
of 205,115 outputs
Outputs of similar age from PLOS ONE
#3,684
of 5,229 outputs
Altmetric has tracked 25,193,883 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 218,525 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.7. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 205,115 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,229 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.