Enhance the Performance of Network Computation by a Tunable Weighting Strategy. Li, H., Bu, Z., Wang, Z., Cao, J., & Shi, Y. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(3):214–223, June, 2018. doi abstract bibtex Networked systems with high computational efficiency are desired in many applications ranging from sociology to engineering. Generally, the performance of the network computation can be enhanced by two ways: rewiring and weighting. In this paper, we proposed a new two-modes weighting strategy based on the concept of communication neighbor graph, which takes use of both the local and global topological properties, e.g., degree centrality, betweenness centrality, and closeness centrality. The weighting strategy includes two modes: In the original mode, it enhances the network synchronizability by increasing the weights of bridge edges; whereas in the inverse version, it increases the significance of community structure by decreasing the weights of bridge edges. The scheme of weighting is controlled by only one parameter, i.e., α, which can be easily performed. We test the effectiveness of our model on a number of artificial benchmark networks as well as realworld ones. To the best of our knowledge, the proposed weighting strategy can outperform the existing methods in improving the performance of network computation.
@article{li_enhance_2018,
title = {Enhance the {Performance} of {Network} {Computation} by a {Tunable} {Weighting} {Strategy}},
volume = {2},
issn = {2471-285X},
doi = {10.1109/TETCI.2018.2829906},
abstract = {Networked systems with high computational efficiency are desired in many applications ranging from sociology to engineering. Generally, the performance of the network computation can be enhanced by two ways: rewiring and weighting. In this paper, we proposed a new two-modes weighting strategy based on the concept of communication neighbor graph, which takes use of both the local and global topological properties, e.g., degree centrality, betweenness centrality, and closeness centrality. The weighting strategy includes two modes: In the original mode, it enhances the network synchronizability by increasing the weights of bridge edges; whereas in the inverse version, it increases the significance of community structure by decreasing the weights of bridge edges. The scheme of weighting is controlled by only one parameter, i.e., α, which can be easily performed. We test the effectiveness of our model on a number of artificial benchmark networks as well as realworld ones. To the best of our knowledge, the proposed weighting strategy can outperform the existing methods in improving the performance of network computation.},
number = {3},
journal = {IEEE Transactions on Emerging Topics in Computational Intelligence},
author = {Li, Hui-Jia and Bu, Zhan and Wang, Zhen and Cao, Jie and Shi, Yong},
month = jun,
year = {2018},
pages = {214--223},
}
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