The degree of a node is a measure of its connectivity (number of connections); eigenvector centrality incorporates both direct and indirect connectivity (how connected a node's immediate connections are); and betweenness is a measure of the bottleneck a particular node forms in the network.
The diameter of this network (maximum distance from one node to another) is 58 and there is an average node connectivity (number of inputs/outputs) of 2.37 (max 37).
Figure 17 Connectivity (number of connected nodes) versus throughput of a single network.
As shown in Fig. 1 (insert), they have a much higher average connectivity (number of first-order protein partners) compared to all interactome proteins.
This suggests that using connectivity number as a criterion of protein complex prediction may be a good approach.
Therefore, we look at the connectivity number of complexes as a possible alternative criterion.
We calculated the connectivity (number of ORFs with a nonzero interaction), interaction mean, and variance for each ORF in the genetic interaction network.
Despite several attempts over the last 10 years to determine the connectivity number, this is still an open question.
Demographic data were used to estimate population size, effective population connectivity (number of successful dispersal events) and a fitness component, lifetime expectancy.
We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs.
