.png){kind=icon}
返回
A self-stabilizing k-clustering algorithm for weighted graphs
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpdc.2010.06.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Mobile ad hoc networks as well as grid platforms are distributed, changing, and error prone environments. Communication costs within such infrastructure can be improved, or at least bounded, by using k-clustering. A k-clustering of a graph, is a partition of the nodes into disjoint sets, called clusters, in which every node is distance at most k from a designated node in its cluster, called the clusterhead. A self-stabilizing asynchronous distributed algorithm is given for constructing a k-clustering of a connected network of processes with unique IDs and weighted edges. The algorithm is comparison based, takes O(nk) time, and uses O(log n + log k) space per process, where n is the size of the network. To the best of our knowledge, this is the first solution to the k-clustering problem on weighted graphs. (c) 2010 Elsevier Inc. All rights reserved.
Keyword:
k-clustering
Self-stabilization
Weighted graph
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
3.8K
被引数:
4.8K
机构
引用论文
A Flexible and Highly Sensitive Pressure Sensor Based on a PDMS Foam Coated with Graphene Nanoplatelets.png)
Sensors
OAAI
SensorsIF0
OAAI分享收藏 没有更多内容
没有更多内容