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Kernel-Based Structural Equation Models for Topology Identification of Directed Networks.

, , and . IEEE Trans. Signal Process., 65 (10): 2503-2516 (2017)

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Centrality-constrained graph embedding., and . ICASSP, page 3113-3117. IEEE, (2013)Kernel-based embeddings for large graphs with centrality constraints., and . ICASSP, page 1901-1905. IEEE, (2015)A proximal gradient algorithm for tracking cascades over networks., , and . ICASSP, page 4778-4782. IEEE, (2014)Topology inference of directed graphs using nonlinear structural vector autoregressive models., , and . ICASSP, page 6513-6517. IEEE, (2017)Kernel-Based Structural Equation Models for Topology Identification of Directed Networks., , and . IEEE Trans. Signal Process., 65 (10): 2503-2516 (2017)Nonlinear structural equation models for network topology inference., , and . CISS, page 163-168. IEEE, (2016)Nonlinear Structural Vector Autoregressive Models With Application to Directed Brain Networks., , and . IEEE Trans. Signal Process., 67 (20): 5325-5339 (2019)Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization., , and . GlobalSIP, page 375-379. IEEE, (2016)Switched dynamic structural equation models for tracking social network topologies., and . GlobalSIP, page 682-686. IEEE, (2015)Dynamic and decentralized learning of overlapping network communities., and . CAMSAP, page 97-100. IEEE, (2015)