Partial Network Identifiability: Theorem Proof and Evaluation
Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley
TL;DR
This paper provides detailed theorem proofs and additional evaluations for network identifiability, focusing on monitor placement strategies to maximize network tomography effectiveness.
Contribution
It offers rigorous proofs and extended evaluations for partial network identifiability, enhancing understanding of monitor placement in network tomography.
Findings
Theorem proofs clarify conditions for network identifiability.
Extended evaluations demonstrate effectiveness of monitor placement strategies.
Results improve network tomography accuracy and reliability.
Abstract
This is a technical report, containing all the theorem proofs and additional evaluations in paper "Monitor Placement for Maximal Identifiability in Network Tomography" by Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley, published in IEEE INFOCOM, 2014.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Sparse and Compressive Sensing Techniques