Albert, R. and Barab si, A.-L., "Error and Attack Tolerance of Complex Networks", Nature 406, 2000. Home/Search Document Not in Database Summary Related Articles Check |
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....lies at the core of most subsequent generalizations. In this model, a node v is influenced by each neighbor w according to a weight bv,w such that w neighbor of v 1. The dynamics of the process then proceed as follows. Each node v chooses a threshold #v uniformly at random from the interval [0, 1]; this represents the weighted fraction of v s neighbors that must become active in order for v to become active. Given a random choice of thresholds, and an initial set of active nodes A0 (with all other nodes inactive) the diffusion process unfolds deterministically in discrete steps: in step ....
....heuristics are commonly used in the sociology literature as estimates of a node s influence [30] The high degree heuristic chooses nodes v in order of decreasing degrees dv . Considering high degree nodes as influential has long been a standard approach for social and other networks [30, 1], and is known in the sociology literature as degree centrality . Distance centrality is another commonly used influence measure in sociology, building on the assumption that a node with short paths to other nodes in a network will have a higher chance of influencing them. Hence, we select ....
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R. Albert, H. Jeong, A. Barabasi. Error and attack tolerance of complex networks. Nature 406(2000), 378-382.
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....networks, which we refer to as the GE model, where all nodes have roughly the same probability to gain new links, and growing preferential (scale free) networks, which we refer to as the Barab asi Albert (BA) model, where nodes with more links are more likely to receive new links. Note that [5] used two general network models, a static random network and a growing preferential network. For our study, we extend the modeling space to a continuum of network models with seniority, adding another dimension in addition to the uniform to preferential dimension. We extend the simulated failure ....
....new parameter # which allows us to simulate more typical scenarios where nature is somewhat malicious, e.g. with # 0.1 (10 attacks) We analyze both static and dynamic susceptibility of the Internet to faults and attacks. In static analysis, we first reconfirm previous work of Albert et al. [5]. Based on these results, we address the problems of existing metrics, the average diameter and the S metric, and propose new network connectivity metrics, K and DIK. Second, we put that result to test by diluting the sequence of faults with a few attacks, which quickly strips scale free networks ....
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R. Albert, H. Jeong, and A. Barab asi, "Error and attack tolerance of complex networks," Nature, vol. 406, pp. 378--382, 2000.
FASD: A Fault-tolerant, Adaptive, Scalable, Distributed Search.. - Kronfol (2002) (Correct)
....runs attempt to emulate the parameters in [18] In the course of metadata key caching and culling, there is a risk that the network will fragment into disconnected components. As a scale free network, see section 4. 6) FASD is very resistant to fragmentation unless exposed to targeted attack [2]. Nevertheless, finding theoretical guarantees against fragmentation is an important avenue for future research. key ref. k hash(2) node 2 k hash(0) node n 1 key ref. k hash(n 2) node n 2 key ref. k hash(n 1) node n 1 k hash(1) Figure 4.1: Bootstrapping in a lattice topology all the n ....
....100 of Nodes Out degree (metadata store size) Figure 4.9: Out degree distribution at the end of the simulation. A large percentage of nodes have low out degree (poorly connected) while a small percentage have large out degree (well connected) See section 4.6 for details. 4. 6 Fault tolerance [2] examines the correlation between a network s connectivity distribution P (k) and its attack tolerance. P (k) is the probability that a node in the network has out degree k. If P (k) decays as a power law (i.e. P (k) k # ) the corresponding network is said to be scale free . In these ....
Albert, R., Jeong, H., et al. Error and attack tolerance of complex networks. Nature, 406 (July 2000), 378--382.
Prediction of Protein Essentiality . . . - Jeong (2002) Self-citation (Albert) (Correct)
.... [15] In a previous study [5] we demonstrated that the architecture of the resulting protein protein interaction network is scale free [16] which implies that highly connected proteins play a more important role in guaranteeing the network s integrity than their less connected counterparts [5, 17]. Correlating this property with known deletion phenotypes we previously found that proteins with more interactions were more likely to be essential than less connected ones [5] To further refine this observation, we selected in each functional class those proteins for which k and their deletion ....
Albert R, Jeong H, Barabsi A-L: Error and attack tolerance of complex networks. Nature
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Albert, R. and Barab si, A.-L., "Error and Attack Tolerance of Complex Networks", Nature 406, 2000.
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R. Albert, H. Jeong, and A. Barab asi, "Error and attack tolerance of complex networks," Nature, vol. 406, pp. 378--382, 2000.
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R. Albert, H. Jeong, and A. Barabasi. Error and attack tolerance in complex networks. Nature, 406:378--382, 2000.
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R. Albert, H. Jeong, and A.-L. Barabasi, "Error and attack tolerance of complex networks," in Nature 406, July 2000.
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