Graph Constrained Label Propagation on Water Supply Networks
نویسندگان
چکیده
منابع مشابه
(Semi-)External Algorithms for Graph Partitioning and Clustering
In this paper, we develop semi-external and external memory algorithms for graph partitioning and clustering problems. Graph partitioning and clustering are key tools for processing and analyzing large complex networks. We address both problems in the (semi-)external model by adapting the size-constrained label propagation technique. Our (semi-)external size-constrained label propagation algori...
متن کاملCommunity Detection using a New Node Scoring and Synchronous Label Updating of Boundary Nodes in Social Networks
Community structure is vital to discover the important structures and potential property of complex networks. In recent years, the increasing quality of local community detection approaches has become a hot spot in the study of complex network due to the advantages of linear time complexity and applicable for large-scale networks. However, there are many shortcomings in these methods such as in...
متن کاملPartitioning Complex Networks via Size-Constrained Clustering
The most commonly used method to tackle the graph partitioning problem in practice is the multilevel approach. During a coarsening phase, a multilevel graph partitioning algorithm reduces the graph size by iteratively contracting nodes and edges until the graph is small enough to be partitioned by some other algorithm. A partition of the input graph is then constructed by successively transferr...
متن کاملGroup 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 ...
متن کاملA Unified View of Graph-based Semi-Supervised Learning: Label Propagation, Graph-Cuts, and Embeddings
Recent years have seen a growing number of graph-based semisupervised learning methods. While the literature currently contains several of these methods, their relationships with one another and with other graph-based data analysis algorithms remain unclear. In this paper, we present a unified view of graph-based semi-supervised learning. Our framework unifies three important and seemingly unre...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013