Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logical models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In ...

Background A great variety of artificial and natural systems can be abstracted into a set of entities interacting with each other. Such abstractions can very well represent the underlying dynamics of the system when modeled as the network of vertices coupled by edges. Prediction of dynamics in these structures based on topological attribute or dependency relations is an important task. Link Pre...

Multiplex networks describe systems whose interactions can be of different nature, and are fundamental to understand complexity beyond the framework simple graphs. Recently it has been pointed out that restricting attention pairwise is also a limitation, as vast majority complex include higher-order strongly affect their dynamics. Here, we propose hyper-diffusion on multiplex networks, dynamica...

In the fiber optic communication network for example,- FTTH, EPON, NBN, OTN and so on, most important components are length between transmitter receiver point. So, to make, cheap meaningful through optical it must be required calculate how what amount of signal is transmitted with given fiber. Finally, said that have several limitations like extension ratio. necessary investigate its affect on ...

In this paper we describe a method to estimate the overflow probability at an arbitrary queue in a network. This work is motivated by the assumption that most network routers will have the capacity to multiplex a moderate to large number of network flows. We first provide our main results on the workload of a queue fed with a large number of traffic flows in a network. Under fairly general cond...