10,521 research outputs found
Underestimated cost of targeted attacks on complex networks
The robustness of complex networks under targeted attacks is deeply connected
to the resilience of complex systems, i.e., the ability to make appropriate
responses to the attacks. In this article, we investigated the state-of-the-art
targeted node attack algorithms and demonstrate that they become very
inefficient when the cost of the attack is taken into consideration. In this
paper, we made explicit assumption that the cost of removing a node is
proportional to the number of adjacent links that are removed, i.e., higher
degree nodes have higher cost. Finally, for the case when it is possible to
attack links, we propose a simple and efficient edge removal strategy named
Hierarchical Power Iterative Normalized cut (HPI-Ncut).The results on real and
artificial networks show that the HPI-Ncut algorithm outperforms all the node
removal and link removal attack algorithms when the cost of the attack is taken
into consideration. In addition, we show that on sparse networks, the
complexity of this hierarchical power iteration edge removal algorithm is only
.Comment: 14 pages, 7 figure
Generalized Network Dismantling
Finding the set of nodes, which removed or (de)activated can stop the spread
of (dis)information, contain an epidemic or disrupt the functioning of a
corrupt/criminal organization is still one of the key challenges in network
science. In this paper, we introduce the generalized network dismantling
problem, which aims to find the set of nodes that, when removed from a network,
results in a network fragmentation into subcritical network components at
minimum cost. For unit costs, our formulation becomes equivalent to the
standard network dismantling problem. Our non-unit cost generalization allows
for the inclusion of topological cost functions related to node centrality and
non-topological features such as the price, protection level or even social
value of a node. In order to solve this optimization problem, we propose a
method, which is based on the spectral properties of a novel node-weighted
Laplacian operator. The proposed method is applicable to large-scale networks
with millions of nodes. It outperforms current state-of-the-art methods and
opens new directions in understanding the vulnerability and robustness of
complex systems.Comment: 6 pages, 5 figure
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