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Efficient target control of complex networks based on preferential matching

View Article: PubMed Central - PubMed

ABSTRACT

Controlling a complex network towards a desired state is of great importance in many applications. Existing works present an approximate algorithm to find the input nodes used to control partial nodes of the network. However, the input nodes obtained by this algorithm depend on the node matching order and cannot achieve optimum results. Here we present a novel algorithm to find the input nodes for target control based on preferential matching. The algorithm elaborately arranges the matching order of the nodes to reduce the size of the input node set. The results on both synthetic and real networks indicate that the proposed algorithm outperforms the previous algorithm.

No MeSH data available.


Results for real networks.We show the fraction of input nodes and the fraction of target nodes. The PM method always achieves better performance in both the local and random target node selection schemes.
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pone.0175375.g005: Results for real networks.We show the fraction of input nodes and the fraction of target nodes. The PM method always achieves better performance in both the local and random target node selection schemes.

Mentions: We also evaluated the performance of the PM algorithm in real networks. The networks are selected based on diversity of topological structure and include food web, transcription, citation, and Internet networks. The results are shown in Table 1 and Fig 5. For all networks and fractions of target nodes in both random and local schemes, the PM algorithm outperforms the GA.


Efficient target control of complex networks based on preferential matching
Results for real networks.We show the fraction of input nodes and the fraction of target nodes. The PM method always achieves better performance in both the local and random target node selection schemes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383299&req=5

pone.0175375.g005: Results for real networks.We show the fraction of input nodes and the fraction of target nodes. The PM method always achieves better performance in both the local and random target node selection schemes.
Mentions: We also evaluated the performance of the PM algorithm in real networks. The networks are selected based on diversity of topological structure and include food web, transcription, citation, and Internet networks. The results are shown in Table 1 and Fig 5. For all networks and fractions of target nodes in both random and local schemes, the PM algorithm outperforms the GA.

View Article: PubMed Central - PubMed

ABSTRACT

Controlling a complex network towards a desired state is of great importance in many applications. Existing works present an approximate algorithm to find the input nodes used to control partial nodes of the network. However, the input nodes obtained by this algorithm depend on the node matching order and cannot achieve optimum results. Here we present a novel algorithm to find the input nodes for target control based on preferential matching. The algorithm elaborately arranges the matching order of the nodes to reduce the size of the input node set. The results on both synthetic and real networks indicate that the proposed algorithm outperforms the previous algorithm.

No MeSH data available.