Demixing and Topology Identification for Mixed Graph Signals

Hong, Jian; Dai, Xianhua

doi:10.1109/iccis53528.2021.9646039

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more challenging problem is the inference of multiple networks where it is not known from which network observed data are generated. We note GSP methods [32], [43]- [45] and statistical methods [46]- [48] within this category. We refer the reader to the reviews in [10], [49], [50] for more examples of multiple network estimation.…”

Section: Related Workmentioning

confidence: 99%

Joint Network Topology Inference via a Shared Graphon Model

Madeline¹,

Segarra²

2022

Preprint

View full text Add to dashboard Cite

We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random graph model, which is a nonparametric model from which graphs of potentially different sizes can be drawn. The versatility of graphons allows us to tackle the joint inference problem even for the cases where the graphs to be recovered contain different number of nodes and lack precise alignment across the graphs. Our solution is based on combining a maximum likelihood penalty with graphon estimation schemes and can be used to augment existing network inference methods. The proposed joint network and graphon estimation is further enhanced with the introduction of a robust method for noisy graph sampling information. We validate our proposed approach by comparing its performance against competing methods in synthetic and realworld datasets.

show abstract