Trophic Flow Networks as Indicators of Ecosystem Stress

Food Webs ◽  
1996 ◽  
pp. 358-368 ◽  
Author(s):  
Robert E. Ulanowicz
Keyword(s):  
Flow Networks

scholarly journals Effects of thermo-erosion gullying on hydrologic flow networks, discharge and soil loss

2014 ◽  
Vol 9 (10) ◽  
pp. 105010 ◽  
Author(s):  
Etienne Godin ◽  
Daniel Fortier ◽  
Stéphanie Coulombe
Keyword(s):  
Soil Loss ◽  
Flow Networks

scholarly journals Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 776 ◽  
Author(s):  
Robert K. Niven ◽  
Markus Abel ◽  
Michael Schlegel ◽  
Steven H. Waldrip
Keyword(s):  
Maximum Entropy ◽  
Joint Probability ◽  
Financial Economic ◽  
Flow Networks ◽  
Flow Network ◽  
Physical Constraints ◽  
Generalized Maximum Entropy ◽  
Deterministic Solution ◽  
Network Properties ◽  
The Cost

The concept of a “flow network”—a set of nodes and links which carries one or more flows—unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include “observable” constraints on various parameters, “physical” constraints such as conservation laws and frictional properties, and “graphical” constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks.

scholarly journals Information Flow Networks of Chinese Stock Market Sectors

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 13066-13077 ◽  
Author(s):  
Peng Yue ◽  
Qing Cai ◽  
Wanfeng Yan ◽  
Wei-Xing Zhou
Keyword(s):  
Stock Market ◽  
Information Flow ◽  
Chinese Stock Market ◽  
Flow Networks

scholarly journals Asymmetric flow networks

2014 ◽  
Vol 237 (2) ◽  
pp. 566-579 ◽  
Author(s):  
Norma Olaizola ◽  
Federico Valenciano
Keyword(s):  
Asymmetric Flow ◽  
Flow Networks

Investments in stochastic maximum flow networks

1991 ◽  
Vol 31 (1) ◽  
pp. 457-467 ◽  
Author(s):  
Richard D. Wollmer
Keyword(s):  
Maximum Flow ◽  
Flow Networks

Emergence of asymmetry in constructal tree flow networks

2005 ◽  
Vol 98 (10) ◽  
pp. 104903 ◽  
Author(s):  
Louis Gosselin ◽  
Adrian Bejan
Keyword(s):  
Flow Networks
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