[PDF] Hodge Decomposition of Information Flow on Complex Networks
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The combinatorial Hodge decomposition theorem decomposes any flow on a network into three components: gradient, harmonic and curl flows. Gradient flows are ...
Dec 1, 2014 · Combinatorial Hodge theory enables us to orthogonally decompose information flow into gradient (unidirectional a-cyclic flow), harmonic (global ...
Feb 2, 2015 · Combinatorial Hodge theory enables us to orthogonally decompose information flow into gradient (unidirectional acyclic flow), harmonic (global ...
The combinatorial Hodge theory enables us to decompose any flow on a network into three mutually orthogonal components: gradient, harmonic and curl flows. ... .
Hodge Decomposition of Information Flow on Small-World Networks
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The combinatorial Hodge theory enables us to decompose any flow on a network into three mutually orthogonal components: gradient, harmonic and curl flows. In ...
Sep 28, 2016 · We decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows.
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Aug 13, 2020 · The combinatorial/discrete HHD generalizes the Helmholtz decomposition to edge flows on networks [15, 16]. ... information about f on the ...
Aug 23, 2024 · The Helmholtz-Hodge-Kodaira decomposition can split them into a rotational and gradient component which reveals the hierarchy of Granger causality flow.
We analyze brain networks by decomposing them into three orthogonal components: gradient, curl, and harmonic flows, through the Hodge decomposition.
Abstract We propose a technique that we call HodgeRank for ranking data that may be incomplete and imbalanced, characteristics common in modern datasets.