To quantitatively measure the connectedness robustness of a complex network, a sequence of values that record the remaining connectedness of the network after a sequence of node- or edge-removal attacks can be used. However, it is computationally time-consuming to measure the network connectedness robustness by attack simulations for large-scale networked systems. In the present paper, an efficient method based on convolutional neural network (CNN) is proposed to train for estimating the network connectedness robustness. The new approach is motivated by the facts that 1) the adjacency matrix of a network can be converted to a gray-scale image and CNN is very powerful for image processing, and 2) CNN has proved very effective in predicting the controllability robustness of complex networks. Extensive experimental studies on directed and undirected, as well as synthetic and real-world networks suggest that: 1) the proposed CNN-based methodology performs excellently in the prediction of the connectedness robustness of complex networks as a process; 2) it performs fairly well as the indicator for the connectedness robustness, compared to other predictive measures.
|Number of pages||11|
|Journal||IEEE Transactions on Network Science and Engineering|
|Early online date||27 Aug 2021|
|Publication status||Published - Oct 2021|
Bibliographical noteThis research was supported in part by the National Natural Science Foundation of China (No. 62002249, 61873167), in part by the Hong Kong Research Grants Council under the GRF Grant CityU11206320, in part by the Open Project Program of the State Key Lab of CAD&CG (A2112), Zhejiang University, and in part by the Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P. R. China (No. Scip202103).
© 2013 IEEE.
- Complex network
- Complex networks
- Computational modeling
- convolutional neural network
- Convolutional neural networks
- Image edge detection
- Measurement uncertainty