Title
Key Nodes Selection in Controlling Complex Networks via Convex Optimization
Abstract
Key nodes are the nodes connected with a given number of external source controllers that result in minimal control cost. Finding such a subset of nodes is a challenging task since it impossible to list and evaluate all possible solutions unless the network is small. In this paper, we approximately solve this problem by proposing three algorithms step by step. By relaxing the Boolean constraints in the original optimization model, a convex problem is obtained. Then inexact alternating direction method of multipliers (IADMMs) is proposed and convergence property is theoretically established. Based on the degree distribution, an extension method named degree-based IADMM (D-IADMM) is proposed such that key nodes are pinpointed. In addition, with the technique of local optimization employed on the results of D-IADMM, we also develop LD-IADMM and the performance is greatly improved. The effectiveness of the proposed algorithms is validated on different networks ranging from Erdös-Rényi networks and scale-free networks to some real-life networks.
Year
DOI
Venue
2021
10.1109/TCYB.2018.2888953
IEEE Transactions on Cybernetics
Keywords
DocType
Volume
Complex networks,key node selection,optimal control
Journal
51
Issue
ISSN
Citations 
1
2168-2267
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Jie Ding1103.56
Changyun Wen23686284.86
Guoqi Li338746.18
Chen Zhenghua414110.59