Under a common dominant left eigenvector condition on logic matrices, signed multi-topic DeGroot-Friedkin dynamics reduce to scalar maps and converge globally to pluralistic, mixed, or vertex-dominant social power configurations while preserving interaction centrality ordering in the first two cases
Dynamics over signed networks
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The paper reviews and extends energy-based dynamical models that use gradient flows and energy landscapes for neurocomputation, learning, and optimization tasks.
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Signed DeGroot-Friedkin Dynamics with Interdependent Topics
Under a common dominant left eigenvector condition on logic matrices, signed multi-topic DeGroot-Friedkin dynamics reduce to scalar maps and converge globally to pluralistic, mixed, or vertex-dominant social power configurations while preserving interaction centrality ordering in the first two cases
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Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
The paper reviews and extends energy-based dynamical models that use gradient flows and energy landscapes for neurocomputation, learning, and optimization tasks.