报告题目: Controllability of complex networks
摘 要：
While a large number of methods for module detection have been
developed for undirected networks, it is difficult to adapt them to handle directed
networks due to the lack of consensus criteria for measuring the node significance
in a directed network. In this paper, we propose a novel structural index, the
control range, motivated by recent studies on the structural controllability of
large-scale directed networks. The control range of a node quantifies the size of
the subnetwork that the node can effectively control. A related index, called the
control range similarity, is also introduced to measure the structural similarity
between two nodes. When applying the index of control range to several real-world and synthetic directed networks, it is observed that the control range of the
nodes is mainly influenced by the network’s degree distribution and that nodes
with a low degree may have a high control range. We use the index of control
range similarity to detect and analyze functional modules in glossary networks
and the enzyme-centric network of homo sapiens. Our results, as compared
with other approaches to module detection such as modularity optimization
algorithm, dynamic algorithm and clique percolation method, indicate that the
proposed indices are effective and practical in depicting structural and modular
characteristics of sparse directed networks.
文章摘要
报告地点：新理科大楼E518
报告时间：2013年5月10日下午3：30
组织者：郑立飞
所属研究所：72886必赢应用数学研究所
欢迎广大师生届时光临！
72886必赢
2013年4月15日
