Attack vulnerability of complex networks.

By Petter Holme, Beom Jun Kim, Chang Yoon, S. Han. Physical review. E, Statistical, nonlinear, and soft matter physics, 2002

1,803 citations recorded by the source corpus.

We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.

Subfields: Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/484df6c4626782c99b796fb96a1ae87459f40278

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Citations in the corpus

A Taxonomy of Botnet Structures (2007, methodology): “In other domains, researchers have noted that small world graphs are essentially random [23].”
Coauthorship networks and patterns of scientific collaboration (2004, background): “[33], who show that collaboration networks are highly susceptible to the removal of the individuals with highest betweenness scores.”
Identifying influential nodes in complex networks (2012, background): “Dangalchev [20] modified the definition to a general form, called residual closeness , which is more sensitive than the well-known measures of vulnerability [21], since it is able to reflect the effects of node removal even if this removal does not result in disconnected components.”
Statistical physics of vaccination (2016, background): “When a node has been vaccinated, the adaptive betweenness centrality is recalculated for the remaining network composed of unvaccinated nodes [297, 298, 306, 314].”
Robustness and resilience of complex networks (2024, neutral): “Such interventions can be informed by topological information, such as those that remove nodes with the largest number of connections or with largest centrality 81,82 , or by non-topological information, such as those informed by node or edge metadata 83 .”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “Many dynamical processes have been studied by complex networks researchers, including synchronization [47, 48], spreading [49, 50], random walks dynamics [51, 52, 53], resilience [11, 54, 55], transportation [56] and avalanches [57].”
Subnetwork hierarchies of biochemical pathways (2002, background): “Deletion in order of recalculated betweenness has been proven particularly effective in some cases (Holme et al., 2002).”
Attack Robustness and Centrality of Complex Networks (2013, background): “The most significant previous study of this latter question is [14], in which the effect of removing vertices both in decreasing order of degree and of betweenness centrality is considered.”
Link Prediction in Complex Networks: A Survey (2010, background): “which has beenwidely used to quantify the functional significance of links subject to various network-based dynamics, such as percolation [47], synchronization [48] and transportation [49].”
Vital nodes identification in complex networks (2016, background): “A slight improvement can be achieved by adaptive recalculation, that is, to choose the node of the largest centrality at first, and then recalculate the centralities of nodes after every step of node removal [189].”
Cascade-based attack vulnerability on the US power grid. (2009, methodology): “Recently, the resilience of real-world networks (Albert et al., 2000; Albert and Barabási, 2002; Holme et al., 2002; Strogatz, 2001; Newman, 2003; Goh et al., 2002) subject to random or intentional attacks has been one of the most central topics in network safety.”
Recommender Systems (2012, methodology): “This index has been used to quantify the functional significance of links subject to various network-based dynamics, such as percolation [147], synchronization [148] and transportation [149].”
Network structure and travel patterns: explaining the geographical disparities of road network vulnerability (2009, background): “It is well known that the topology of a network has a significant impact on its ability to withstand node and link failures (Baran, 1964; Holme et al., 2002).”