The edges of the network are the road sections connecting the nodes, including relevant information about the road as edge variables.
The quadratic matching problem (QMP) asks for a matching in a graph that optimises a quadratic objective in the edge variables.
For the formulation in the natural space of edge variables we show that facet defining inequalities of the underlying polytope can be derived from the deterministic counterparts.
Our results suggest that ERGM-based imputation of edge variables is a viable approach to the analysis of complex studies such as Add Health, provided that care is used in understanding and accounting for the study design.
Building on an arbitrary set of feasible network topologies, e.g., all spanning trees, we explicitly model the power consumption at a given node as a weighted maximum over edge variables.
Analyses explained 38 60% of the total variation in edge variables.
Second, when the GLM analyses were repeated but with tree mortality included as a potential predictor, model performance improved (explaining 42 91% of the variation in edge parameters) and tree mortality was a significant predictor for all edge variables except liana abundance (Table S2).
The second set of constraints relates vertex variables to edge variables, allowing the objective function to be expressed in terms of finding a maximum edge-weight clique.
The FEVT (Falling-Edge, Variable Threshold) algorithm automates detection of slow wave activation times in GEMS.
FEVT, Falling-edge variable threshold; GEMS, Gastrointestinal electrical mapping suite; GI, Gastrointestinal; GUI, Graphical user interface; HR, High-resolution; ICC, Interstitial cells of Cajal; REGROUPS, Region-growing using polynomial surface estimate stablization; SIV, Spatial interpolation and visualization.
