| Title |
Relations between the set-complexity and the structure of graphs and their sub-graphs
|
|---|---|
| Published in |
EURASIP Journal on Bioinformatics & Systems Biology, September 2012
|
| DOI | 10.1186/1687-4153-2012-13 |
| Pubmed ID | |
| Authors |
Tomasz M Ignac, Nikita A Sakhanenko, David J Galas |
| Abstract |
: We describe some new conceptual tools for the rigorous, mathematical description of the "set-complexity" of graphs. This set-complexity has been shown previously to be a useful measure for analyzing some biological networks, and in discussing biological information in a quantitative fashion. The advances described here allow us to define some significant relationships between the set-complexity measure and the structure of graphs, and of their component sub-graphs. We show here that modular graph structures tend to maximize the set-complexity of graphs. We point out the relationship between modularity and redundancy, and discuss the significance of set-complexity in this regard. We specifically discuss the relationship between complexity and entropy in the case of complete-bipartite graphs, and present a new method for constructing highly complex, binary graphs. These results can be extended to the case of ternary graphs, and to other multi-edge graphs, which are fundamentally more relevant to biological structures and systems. Finally, our results lead us to an approach for extracting high complexity modular graphs from large, noisy graphs with low information content. We illustrate this approach with two examples. |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 50% |
| Student > Bachelor | 2 | 25% |
| Other | 1 | 13% |
| Student > Master | 1 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 38% |
| Biochemistry, Genetics and Molecular Biology | 2 | 25% |
| Computer Science | 2 | 25% |
| Economics, Econometrics and Finance | 1 | 13% |
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 21 September 2012.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#42
of 53 outputs
Outputs of similar age
#170,036
of 189,301 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 2 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 53 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 1st percentile – i.e., 1% 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 189,301 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them