.png){kind=icon}
返回
UPDATING AND DOWNDATING TECHNIQUES FOR OPTIMIZING NETWORK COMMUNICABILITY
.png){kind=icon}
OA
AI
DOI：10.1137/140991923.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The total communicability of a network (or graph) is defined as the sum of the entries in the exponential of the adjacency matrix of the network, possibly normalized by the number of nodes. This quantity offers a good measure of how easily information spreads across the network, and can be useful in the design of networks having certain desirable properties. The total communicability can be computed quickly even for large networks using techniques based on the Lanczos algorithm. In this work we introduce some heuristics that can be used to add, delete, or rewire a limited number of edges in a given sparse network so that the modified network has a large total communicability. To this end, we introduce new edge centrality measures, which can be used as a guide in the selection of edges to be added or removed. Moreover, we show experimentally that the total communicability provides an effective and easily computable measure of how well-connected a sparse network is.
Keyword:
network analysis
eigenvector centrality
subgraph centrality
total communicability
edge centrality
free energy
natural connectivity
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:
2.6
论文数:
5.1K
被引数:
1.8W
机构
引用论文
.png){kind=icon}
Evaluating noise in social context: The effect of procedural unfairness on noise annoyance judgments
PREAI
分享收藏分享收藏
查看更多内容