3,260 research outputs found
Controlling edge dynamics in complex networks
The interaction of distinct units in physical, social, biological and
technological systems naturally gives rise to complex network structures.
Networks have constantly been in the focus of research for the last decade,
with considerable advances in the description of their structural and dynamical
properties. However, much less effort has been devoted to studying the
controllability of the dynamics taking place on them. Here we introduce and
evaluate a dynamical process defined on the edges of a network, and demonstrate
that the controllability properties of this process significantly differ from
simple nodal dynamics. Evaluation of real-world networks indicates that most of
them are more controllable than their randomized counterparts. We also find
that transcriptional regulatory networks are particularly easy to control.
Analytic calculations show that networks with scale-free degree distributions
have better controllability properties than uncorrelated networks, and
positively correlated in- and out-degrees enhance the controllability of the
proposed dynamics.Comment: Preprint. 24 pages, 4 figures, 2 tables. Source code available at
http://github.com/ntamas/netctr
Predicting Network Controllability Robustness: A Convolutional Neural Network Approach
Network controllability measures how well a networked system can be
controlled to a target state, and its robustness reflects how well the system
can maintain the controllability against malicious attacks by means of
node-removals or edge-removals. The measure of network controllability is
quantified by the number of external control inputs needed to recover or to
retain the controllability after the occurrence of an unexpected attack. The
measure of the network controllability robustness, on the other hand, is
quantified by a sequence of values that record the remaining controllability of
the network after a sequence of attacks. Traditionally, the controllability
robustness is determined by attack simulations, which is computationally time
consuming. In this paper, a method to predict the controllability robustness
based on machine learning using a convolutional neural network is proposed,
motivated by the observations that 1) there is no clear correlation between the
topological features and the controllability robustness of a general network,
2) the adjacency matrix of a network can be regarded as a gray-scale image, and
3) the convolutional neural network technique has proved successful in image
processing without human intervention. Under the new framework, a fairly large
number of training data generated by simulations are used to train a
convolutional neural network for predicting the controllability robustness
according to the input network-adjacency matrices, without performing
conventional attack simulations. Extensive experimental studies were carried
out, which demonstrate that the proposed framework for predicting
controllability robustness of different network configurations is accurate and
reliable with very low overheads.Comment: 11 pages, 7 figure
CNN-based Prediction of Network Robustness With Missing Edges
Connectivity and controllability of a complex network are two important
issues that guarantee a networked system to function. Robustness of
connectivity and controllability guarantees the system to function properly and
stably under various malicious attacks. Evaluating network robustness using
attack simulations is time consuming, while the convolutional neural network
(CNN)-based prediction approach provides a cost-efficient method to approximate
the network robustness. In this paper, we investigate the performance of
CNN-based approaches for connectivity and controllability robustness
prediction, when partial network information is missing, namely the adjacency
matrix is incomplete. Extensive experimental studies are carried out. A
threshold is explored that if a total amount of more than 7.29\% information is
lost, the performance of CNN-based prediction will be significantly degenerated
for all cases in the experiments. Two scenarios of missing edge representations
are compared, 1) a missing edge is marked `no edge' in the input for
prediction, and 2) a missing edge is denoted using a special marker of
`unknown'. Experimental results reveal that the first representation is
misleading to the CNN-based predictors.Comment: In Proceedings of the IEEE 2022 International Joint Conference on
Neural Networks (IJCNN
Building Damage-Resilient Dominating Sets in Complex Networks against Random and Targeted Attacks
We study the vulnerability of dominating sets against random and targeted
node removals in complex networks. While small, cost-efficient dominating sets
play a significant role in controllability and observability of these networks,
a fixed and intact network structure is always implicitly assumed. We find that
cost-efficiency of dominating sets optimized for small size alone comes at a
price of being vulnerable to damage; domination in the remaining network can be
severely disrupted, even if a small fraction of dominator nodes are lost. We
develop two new methods for finding flexible dominating sets, allowing either
adjustable overall resilience, or dominating set size, while maximizing the
dominated fraction of the remaining network after the attack. We analyze the
efficiency of each method on synthetic scale-free networks, as well as real
complex networks
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