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...length remains below 20 even when up to 30% of nodes fail. 5.4 Small-World Model The scalability and fault-tolerance characteristics of Freenet can be explained in terms of a small-world network model=-=[23,31,22,3]-=-. In a small-world network, the majority of nodes have only relatively few, local, connections to other nodes, while a small number of nodes have large, wide-ranging sets of connections. Small-world n...

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...ighbor w according to a weight bv,w such that # w neighbor of v bv,w # 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 o...

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...ventions, an error tolerance attributed to the robustness of the underlying metabolic network. The ability of nodes to communicate is unaffected even by very high failure rates in scale free networks(=-=Albert et al., 2000-=-). But error tolerance comes at a high price in that these networks are extremely vulnerable to attacks, i.e. to the selection and removal of a few nodes that play a vital role in maintaining the netw...

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... for Unstructured P2P networks are discussed in [93]: iterative deepening, directed BFS and local indices. Networks with power-law organizational structure, display an unexpected degree of robustness =-=[94]-=-, i.e. the ability of the peers to communicate unaffectedly even by extremely high failure rates. But these networks are prone to attacks. Thus, Unstructured P2P networks reduce the network dependence...

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...l, but it is very difficult to get a power-law degree distribution from a network without the power-law decaying degree distribution. 4.2 Metrics of interest We begin the analysis of online social network topologies by looking at their degree distributions. Networks of a power-law degree distribution, P (k) ∼ k−γ , where k is the node degree and γ ≤ 3, attest to the existence of a relatively small number of nodes with a very large number of links. These networks also have distinguishing properties, such as vanishing epidemic threshold, ultra-small worldness, and robustness under random errors [11, 12, 13, 14]. The degree distribution is often plotted as a complementary cumulative probability function (CCDF), ℘(k) ≡ R ∞ k P (k′)dk′ ∼ k−α ∼ k−(γ−1). As a power-law distribution shows up as a straight WWW 2007 / Track: Semantic Web Session: Semantic Web and Web 2.0 837 line in a log-log plot, the exponent of a power-law distribution is a representative characteristic, distinguishing one from others. Next, we examine the clustering coefficient. The clustering coefficient of a node is the ratio of the number of existing links over the number of possible links between its neighbors. Given a network G = (...

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...d co-workers have shown that metabolic networks are organized in a scale-free manner, indicating a high robustness against random errors (e.g. mutations) but a high vulnerability to directed attacks (=-=Albert et al., 2000-=-; Jeong et al., 2000). Conclusions Metabolomic analyses have just begun. With today’s powerful analytical and computational systems, the experimental outcome of systematic changes to plant systems can...