Seven real-world complex networks and one synthetic network are used to evaluate the algorithm.
As in our synthetic network tests, we observe a decline in (text {OAS}_{text {mg}}) as (p_{text {neg}}) increases.
Experimentally, we evaluate our approach using both synthetic network influence games and real-world settings of general interest, each corresponding to a separate branch of the U.S. Government.
The synthetic network geometries are so designed which can evaluate the algorithm in the presence of perfect or imperfect ranges or in case of incomplete data.
For each synthetic network (small-world, scale-free), we considered seeding at various fractions of the budget greedy seeding required to obtain a complete cascade in (G).
One synthetic network used for testing is a set of random Erdös-Rényi (ER) graphs produced and connected together to from a connected network by choosing randomly members to act as boundary nodes, shown in Figure 2. The other synthetic network is produced from connecting independent communities graphs produced using preferential attachment, shown in Figure 3.
Here, we describe a systematic approach that enables integration and transduction of multiple mammalian transcription factor inputs by a synthetic network.
Realization of integral control in a synthetic network is an attractive prospect, but the nature of biochemical networks can make the implementation of even basic control structures challenging.
We built in the yeast Saccharomyces cerevisiae a synthetic network, IRMA, for in vivo "benchmarking" of reverse-engineering and modeling approaches.
The numerical results of implementing the developed modeling and solution approach to a synthetic network and a real-world example application justify its theoretical and practical value.
Nodes above the synthetic network nodes in Figure 5 are connected to lower degree nodes and have higher leverage than synthetic network nodes with the same degree.
