8 research outputs found
Identifying effective multiple spreaders by coloring complex networks
How to identify influential nodes in social networks is of theoretical
significance, which relates to how to prevent epidemic spreading or cascading
failure, how to accelerate information diffusion, and so on. In this Letter, we
make an attempt to find \emph{effective multiple spreaders} in complex networks
by generalizing the idea of the coloring problem in graph theory to complex
networks. In our method, each node in a network is colored by one kind of color
and nodes with the same color are sorted into an independent set. Then, for a
given centrality index, the nodes with the highest centrality in an independent
set are chosen as multiple spreaders. Comparing this approach with the
traditional method, in which nodes with the highest centrality from the
\emph{entire} network perspective are chosen, we find that our method is more
effective in accelerating the spreading process and maximizing the spreading
coverage than the traditional method, no matter in network models or in real
social networks. Meanwhile, the low computational complexity of the coloring
algorithm guarantees the potential applications of our method.Comment: 6 pages, 6 figure
Spreading to localized targets in complex networks.
As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank the spreading ability of nodes in the network. To this end, substantial effort has been made and a variety of effective methods have been proposed. These methods usually define the spreading ability of a node as the number of finally infected nodes given that the spreading is initialized from the node. However, in many real cases such as advertising and news propagation, the spreading only aims to cover a specific group of nodes. Therefore, it is necessary to study the spreading ability of nodes towards localized targets in complex networks. In this paper, we propose a reversed local path algorithm for this problem. Simulation results show that our method outperforms the existing methods in identifying the influential nodes with respect to these localized targets. Moreover, the influential spreaders identified by our method can effectively avoid infecting the non-target nodes in the spreading process.We thank an anonymous reviewer for helpful suggestions which improve this paper. This work is supported by the National Natural Science Foundation of China (Nos 61603046 and 11547188), Natural Science Foundation of Beijing (No. 16L00077) and the Young Scholar Program of Beijing Normal University (No. 2014NT38)