Researchers have developed a novel method for inferring directed graph topologies from nodal measurements generated by linear diffusion dynamics. This approach models observations as outputs of a graph convolutional filter, addressing challenges where the graph-shift operator and covariance matrix are not simultaneously diagonalizable. The proposed algorithms identify the diffusion filter by solving quadratic matrix equations and then determine the network topology by finding a sparse shift that commutes with the estimated filter. Numerical tests demonstrate the effectiveness of these algorithms on synthetic and real-world data, with potential applications in urban mobility analysis and portfolio optimization. AI
IMPACT Introduces a new algorithmic approach for network inference, potentially improving applications in complex system analysis.
RANK_REASON The cluster contains an academic paper published on arXiv detailing a new methodology in machine learning.
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- Directed Graph Topology Inference via Graph Filter Identification
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