Linear structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagation. However, these approaches are limited because they assume linear dependence among observable variables. The present paper advocates a more general nonlinear structural equation model based on polynomial expansions, which compensates for possible nonlinear dependencies between network nodes. To this end, a group-sparsity regularized estimator is put forth to leverage the inherent edge sparsity that is present in most real-world networks. A novel computationally-efficient proximal gradient algorithm is developed to estimate the polynomial SEM coefficients, and hence infer the edge structure. Preliminary tests on simulated data demonstrate the effectiveness of the novel approach.
|Original language||English (US)|
|Title of host publication||2016 50th Annual Conference on Information Systems and Sciences, CISS 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Apr 26 2016|
|Event||50th Annual Conference on Information Systems and Sciences, CISS 2016 - Princeton, United States|
Duration: Mar 16 2016 → Mar 18 2016
|Name||2016 50th Annual Conference on Information Systems and Sciences, CISS 2016|
|Other||50th Annual Conference on Information Systems and Sciences, CISS 2016|
|Period||3/16/16 → 3/18/16|
Bibliographical noteFunding Information:
Work in this paper was supported by NSF 1500713, and NIH 1R01GM104975-01.
- Network topology inference
- Nonlinear modeling
- Structural equation models (SEMs)