Network inference from consensus dynamics
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1708.05329.pdf
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1.64 MB
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Adobe PDF
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Author(s)
Segarra, Santiago M
Schaub, Michael T
Jadbabaie-Moghadam, Ali
Date Issued
January 2018
Journal
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Segarra, Santiago, et al. “Network Inference from Consensus Dynamics.” 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 12-15 December, 2017, Melbourne, Australia, IEEE, 2017, pp. 3212–17.
Version
Author's final manuscript
Abstract
We consider the problem of identifying the topology of a weighted, undirected network G from observing snapshots of multiple independent consensus dynamics. Specifically, we observe the opinion profiles of a group of agents for a set of M independent topics and our goal is to recover the precise relationships between the agents, as specified by the unknown network G. In order to overcome the under-determinacy of the problem at hand, we leverage concepts from spectral graph theory and convex optimization to unveil the underlying network structure. More precisely, we formulate the network inference problem as a convex optimization that seeks to endow the network with certain desired properties - such as sparsity - while being consistent with the spectral information extracted from the observed opinions. This is complemented with theoretical results proving consistency as the number M of topics grows large. We further illustrate our method by numerical experiments, which showcase the effectiveness of the technique in recovering synthetic and real-world networks.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC.2017.8264130
