With LTR and TRANSWESDs,w,2 we considered local variants of TR removing an edge only if there is an explaining path of length 2. Whereas TRANSWESDs,w,2 performed well for the DREAM challenge but unfavorable for the SysGenSIM data, weighted LTR yielded superior performance in almost all benchmark tests and only (signed or unsigned) TRANSWESD applied to all paths could deliver comparable results.
The removal of edges presented a specific challenge because after removing an edge the current tree structure gave little information about how to restore dominance.
Each update to the minimum heap (like removing an edge or updating the assortativity weight of an edge) takes O logE) time.
In this paper, we used the assumption that chemical graphs we treat contain only atoms with valence at most 4 (except benzene rings) in order to define the parent of a 1-augmented tree G as a 0-augmented tree T that is obtained by removing an edge corresponding to a single bond in G.
We introduce a rule to choose one of them based only on the topological information on G and C. For each vertex v in C, let N v) denote the set of vertices in V−V(C) adjacent to v. Removing an edge vw with v∈V(C) and w∈N v) leaves a multi-tree containing w, which we denote by T w.
However, it is not difficult to extend our enumeration algorithm for chemical graphs possibly with atoms with valence more than 4 just by modifying the definition so that the parent of a 1-augmented tree G is allowed to be a 0-augmented tree T obtained by removing an edge that corresponds to a double or triple bond in G.
What constitutes a single step on the adaptive landscape is now significantly different than a model that directly connects or disconnects the regulatory interactions by adding or removing an edge or changing the weight in a connection matrix.
When removing an edge from R to M, we may remove some edges in ℐ.
If removing an edge in the global network improved the score then the disconnected edge was labeled as non-predictive.
Perturbations to the network can be performed by clamping a node to state "0", for deleting a gene, or removing an edge, for disrupting an interaction.
One simple heuristic search algorithm is greedy hill-climbing algorithm, where three types of operators are defined to change one edge at each step: adding an edge, removing an edge, and reversing an edge.
