Open AccessJoint Network Topology and Dynamics Recovery From Perturbed Stationary Points
Author(s) -
Hoi-To Wai,
Anna Scaglione,
Baruch Barzel,
Amir Leshem
Publication year - 2019
Publication title -
ieee transactions on signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.638
H-Index - 270
eISSN - 1941-0476
pISSN - 1053-587X
DOI - 10.1109/tsp.2019.2929471
Subject(s) - identifiability , computer science , network topology , inference , network dynamics , topology (electrical circuits) , verifiable secret sharing , graph , complex network , theoretical computer science , mathematical optimization , mathematics , artificial intelligence , machine learning , set (abstract data type) , discrete mathematics , combinatorics , world wide web , programming language , operating system
This paper presents an inference method to learn a model for complex system based on observations of the perturbed stationary points. We propose to jointly estimate the dynamics parameters and network topology through a regularized regression formulation. A distinguished feature of our approach rests on the direct modeling of rank deficient network data, which is widely found in network science bu...
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom