DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference

Using the DART package: Denoising Algorithm based on Relevance network Topology

Loss-based Inference of Multicast Network Topology

The use of multicast inference on end-to-end measurement has recently been proposed as a means to infer network internal characteristics such as packet loss rate and network topology. In this paper we propose and evaluate new algorithms for multicast topology inference based on measurement of end-to-end loss. We compare their accuracy and comment on their computational complexity.

Network (Tree) Topology Inference Based on Prufer Sequence

Network topology discovery is the basis for any network management application. The problem of estimating internal structure and link-level performance from end-to-end measurements is known as network tomography. This paper proposes a novel approach to discover network characteristics, in particular, tree topology from end-to-end path metrics between OD (Origin – Destination) pairs. The approac...

Sensor Network Topology Inference

We present a method for inferring the topology of a sensor network given non-discriminating observations of activity in the monitored region. This is accomplished based on no prior knowledge of the relative locations of the sensors and weak assumptions regarding environmental conditions. Our approach employs a two-level reasoning system made up of a stochastic Expectation Maximization algorithm...

Topology Inference using Network Coding

The network coding paradigm is based on the idea that independent information flows can be linearly combined throughout the network to give benefits in terms of throughput, complexity etc. In this paper, we explore the application of the network coding paradigm to topology inference. Our goal is to infer the topology of a network using multiple sources and observations at the edges of the netwo...