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Found 6 papers, 0 papers with code
Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks
The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the well-known parallel factor (PARAFAC) decomposition.
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity
To unify these complementary perspectives, linear structural vector autoregressive models (SVARMs) that leverage both contemporaneous and time-lagged nodal data have recently been put forth.
Tracking Switched Dynamic Network Topologies from Information Cascades
Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies.
Kernel-Based Structural Equation Models for Topology Identification of Directed Networks
Interestingly, pursuit of the novel kernel-based approach yields a convex regularized estimator that promotes edge sparsity, and is amenable to proximal-splitting optimization methods.
Joint community and anomaly tracking in dynamic networks
Efficient tracking algorithms suitable for both online and decentralized operation are developed.
Embedding Graphs under Centrality Constraints for Network Visualization
Visual rendering of graphs is a key task in the mapping of complex network data.
