Biomedical subjects

Nong Ye

Publications and source records attributed to Nong Ye.

8 recordsLinked to original sources

PubMed2005

Tolerance of scale-free networks against attack-induced cascades.

Scale-free networks can be disintegrated by attack on a single or a very few nodes through the process of cascading failures. By utilizing a prototype cascading model, we previously determined the critical value of the capacity parameter below which the network can become disintegrated due to attack on a single node. A fundamental question in network security, which has not been addressed previously but may be more important and of wider interest, is how to design networks of finite capacity that are safe against cascading breakdown. Here we derive an upper bound for the capacity parameter, above which the network is immune to cascading breakdown. Our theory also yields estimates for the maximally achievable network integrity via controlled removal of a small set of low-degree nodes. The theoretical results are confirmed numerically.

Journal Article↗

PubMed2005

Self-organized scale-free networks.

Growth and preferential attachments have been coined as the two fundamental mechanisms responsible for the scale-free feature in complex networks, as characterized by an algebraic degree distribution. There are situations, particularly in biological networks, where growth is absent or not important, yet some of these networks still exhibit the scale-free feature with a small degree exponent. Here we propose two classes of models to account for this phenomenon. We show analytically and numerically that, in the first model, a spectrum of algebraic degree distributions with a small exponent can be generated. The second model incorporates weights for nodes, and it is able to generate robust scale-free degree distribution with larger algebraic exponents. Our results imply that it is natural for a complex network to self-organize itself into a scale-free state without growth.

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PubMed2005

Jamming in complex gradient networks.

Flows of physical quantities in large complex networks, natural or man made, rely in general on some scalar gradients existing in the networks. We investigate, analytically and numerically, under what conditions jamming in gradient flows can occur in random and scale-free networks. We find that the degree of jamming typically increases with the average connectivity of the network. A crossover phenomenon is uncovered where for < k(c) (k(c) denotes a critical connectivity, estimated to be about 10), scale-free networks have a higher level of congestion than random networks with the same , while the opposite occurs for > k(c).

Journal Article↗

PubMed2005

Onset of traffic congestion in complex networks.

Free traffic flow on a complex network is key to its normal and efficient functioning. Recent works indicate that many realistic networks possess connecting topologies with a scale-free feature: the probability distribution of the number of links at nodes, or the degree distribution, contains a power-law component. A natural question is then how the topology influences the dynamics of traffic flow on a complex network. Here we present two models to address this question, taking into account the network topology, the information-generating rate, and the information-processing capacity of individual nodes. For each model, we study four kinds of networks: scale-free, random, and regular networks and Cayley trees. In the first model, the capacity of packet delivery of each node is proportional to its number of links, while in the second model, it is proportional to the number of shortest paths passing through the node. We find, in both models, that there is a critical rate of information generation, below which the network traffic is free but above which traffic congestion occurs. Theoretical estimates are given for the critical point. For the first model, scale-free networks and random networks are found to be more tolerant to congestion. For the second model, the congestion condition is independent of network size and topology, suggesting that this model may be practically useful for designing communication protocols.

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PubMed2004

Characterization of weighted complex networks.

To account for possible distinct functional roles played by different nodes and links in complex networks, we introduce and analyze a class of weighted scale-free networks. The weight of a node is assigned as a random number, based on which the weights of links are defined. We utilize the concept of betweenness to characterize the weighted networks and obtain the scaling laws governing the betweenness as functions both of the weight and of the degree. The scaling results may be useful for identifying influential nodes in terms of physical functions in complex networks.

Journal Article↗

PubMed2003

Propagation and immunization of infection on general networks with both homogeneous and heterogeneous components.

We consider the entire spectrum of architectures of general networks, ranging from being heterogeneous (scale-free) to homogeneous (random), and investigate the infection dynamics by using a three-state epidemiological model that does not involve the mechanism of self-recovery. This model is relevant to realistic situations such as the propagation of a flu virus or information over a social network. Our heuristic analysis and computations indicate that (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected and (2) heterogeneous networks are relatively more robust against spreads of infection as compared with homogeneous networks. We have also considered the problem of immunization for preventing wide spread of infection, with the result that targeted immunization is effective for heterogeneous networks.

Animals↗

PubMed2003

A data mining technique for discovering distinct patterns of hand signs: implications in user training and computer interface design.

Hand signs are considered as one of the important ways to enter information into computers for certain tasks. Computers receive sensor data of hand signs for recognition. When using hand signs as computer inputs, we need to (1) train computer users in the sign language so that their hand signs can be easily recognized by computers, and (2) design the computer interface to avoid the use of confusing signs for improving user input performance and user satisfaction. For user training and computer interface design, it is important to have a knowledge of which signs can be easily recognized by computers and which signs are not distinguishable by computers. This paper presents a data mining technique to discover distinct patterns of hand signs from sensor data. Based on these patterns, we derive a group of indistinguishable signs by computers. Such information can in turn assist in user training and computer interface design.

Algorithms↗

PubMed2002

Statistical properties and attack tolerance of growing networks with algebraic preferential attachment.

We consider growing networks with algebraic preferential attachment and address two questions: (1) what is the effect of temporal fluctuations in the number of new links acquired by the network? and (2) what is the network tolerance against random failures and intentional attacks? We find that the fluctuations generally have little effect on the network properties, although they lead to a plateau behavior for small degrees in the connectivity distribution. Formulas are derived for the evolution and distribution of the network connectivity, which are tested by numerical simulations. Numerical study of the effect of failures and attacks suggests that networks constructed under algebraic preferential attachment are more robust than scale-free networks.

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