Graph constrained label propagation on water supply networks

Graph Constrained Label Propagation on Water Supply Networks

Group Sparsity Constrained Automatic Brain Label Propagation

In this paper, we present a group sparsity constrained patch based label propagation method for multi-atlas automatic brain labeling. The proposed method formulates the label propagation process as a graph-based theoretical framework, where each voxel in the input image is linked to each candidate voxel in each atlas image by an edge in the graph. The weight of the edge is estimated based on a ...

Discriminant Analysis with Label Constrained Graph Partition

In this paper, a space partition method called “Label Constrained Graph Partition” (LCGP) is presented to solve the Sample-InterweavingPhenomenon in the original space. We first divide the entire training set into subclasses by means of LCGP, so that the scopes of subclasses will not overlap in the original space. Then “Most Discriminant Subclass Distribution” (MDSD) criterion is proposed to de...

Head-Driven Simulation of Water Supply Networks

Up to now most of the existing water supply network analyses have been based on demand-driven simulation models. These models assume that nodal outflows are fixed and are always available. However, this method of simulation neglects the pressure-dependent nature of demand that is characterized by changes in actual nodal outflows particularly during critical events like major mechanical or hydra...

Detecting Community Structure by Using a Constrained Label Propagation Algorithm

Community structure is considered one of the most interesting features in complex networks. Many real-world complex systems exhibit community structure, where individuals with similar properties form a community. The identification of communities in a network is important for understanding the structure of said network, in a specific perspective. Thus, community detection in complex networks ga...