Predicting future traffic using hidden markov models
Network traffic volume estimation and prediction is an important research topic that attracts
persistent attention from the networking community and the machine learning community …
persistent attention from the networking community and the machine learning community …
Efficient identification of additive link metrics via network tomography
We investigate the problem of identifying individual link metrics in a communication network
from accumulated end-to-end metrics over selected measurement paths, under the …
from accumulated end-to-end metrics over selected measurement paths, under the …
Semi-blind inference of topologies and dynamical processes over dynamic graphs
VN Ioannidis, Y Shen… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
A task of major practical importance in network science is inferring the graph structure from
noisy observations at a subset of nodes. Available methods for topology inference typically …
noisy observations at a subset of nodes. Available methods for topology inference typically …
A survey on network tomography with network coding
P Qin, B Dai, B Huang, G Xu… - … Communications Surveys & …, 2014 - ieeexplore.ieee.org
The overhead of internal network monitoring motivates techniques of network tomography.
Network coding (NC) presents a new opportunity for network tomography as NC introduces …
Network coding (NC) presents a new opportunity for network tomography as NC introduces …
Inferring link metrics from end-to-end path measurements: Identifiability and monitor placement
We investigate the problem of identifying individual link metrics in a communication network
from end-to-end path measurements, under the assumption that link metrics are additive and …
from end-to-end path measurements, under the assumption that link metrics are additive and …
Identifiability of link metrics based on end-to-end path measurements
We investigate the problem of identifying individual link metrics in a communication network
from end-to-end path measurements, under the assumption that link metrics are additive and …
from end-to-end path measurements, under the assumption that link metrics are additive and …
On identifying additive link metrics using linearly independent cycles and paths
A Gopalan, S Ramasubramanian - IEEE/ACM Transactions on …, 2011 - ieeexplore.ieee.org
In this paper, we study the problem of identifying constant additive link metrics using linearly
independent monitoring cycles and paths. A monitoring cycle starts and ends at the same …
independent monitoring cycles and paths. A monitoring cycle starts and ends at the same …
Network loss inference with second order statistics of end-to-end flows
We address the problem of calculating link loss rates from end-to-end measurements.
Contrary to existing works that use only the average end-to-end loss rates or strict temporal …
Contrary to existing works that use only the average end-to-end loss rates or strict temporal …
Monitor placement for maximal identifiability in network tomography
We investigate the problem of placing a given number of monitors in a communication
network to identify the maximum number of link metrics from end-to-end measurements …
network to identify the maximum number of link metrics from end-to-end measurements …
Objective supervised machine learning-based classification and inference of biological neuronal networks
The classification of biological neuron types and networks poses challenges to the full
understanding of the human brain's organisation and functioning. In this paper, we develop …
understanding of the human brain's organisation and functioning. In this paper, we develop …