Performance of attack strategies on modular networks
August 08, 2016 Β· Declared Dead Β· π J. Complex Networks
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Bruno RequiΓ£o da Cunha, SebastiΓ‘n GonΓ§alves
arXiv ID
1608.02619
Category
physics.soc-ph
Cross-listed
cs.SI
Citations
8
Venue
J. Complex Networks
Last Checked
3 months ago
Abstract
Vulnerabilities of complex networks have became a trend topic in complex systems recently due to its real world applications. Most real networks tend to be very fragile to high betweenness adaptive attacks. However, recent contributions have shown the importance of interconnected nodes in the integrity of networks and module-based attacks have appeared promising when compared to traditional malicious non-adaptive attacks. In the present work we deeply explore the trade-off associated with attack procedures, introducing a generalized robustness measure and presenting an attack performance index that takes into account both robustness of the network against the attack and the run-time needed to obtained the list of targeted nodes for the attack. Besides, we introduce the concept of deactivation point aimed to mark the point at which the network stops to function properly. We then show empirically that non-adaptive module-based attacks perform better than high degree and betweenness adaptive attacks in networks with well defined community structures and consequent high modularity.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.soc-ph
π
π
The Cartographer
R.I.P.
π»
Ghosted
Networks beyond pairwise interactions: structure and dynamics
R.I.P.
π»
Ghosted
Statistical physics of human cooperation
R.I.P.
π»
Ghosted
Vital nodes identification in complex networks
R.I.P.
π»
Ghosted
Influence maximization in complex networks through optimal percolation
R.I.P.
π»
Ghosted
Scale-free networks are rare
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted