Differentiating complex network models: An engineering perspective

AbstractNetwork models that can capture the underlying network’s topologies and functionalities are crucial for the development of complex network algorithms and protocols. In the engineering community, the performances of network algorithms and protocols are usually evaluated by running them on a network model. In most if not all reported work, the criteria used to determine such a network model rely on how close it matches the network data in terms of some basic topological characteristics. However, the intrinsic relations between a network topology and its functionalities are still unclear. A question arises naturally: For a network model which can reproduce some topological characteristics of the underlying network, is it reasonable and valid to use this model to be a test-bed for evaluating the network’s performances? To answer this question, we take a close look at several typical complex network models of the AS-level Internet as examples of study. We find that although a model can represent the Internet in terms of topological metrics, it cannot be used to evaluate the Internet performances. Our findings reveal that the approaches using topological metrics to discriminate network models, which have been widely used in the engineering community, may lead to confusing or even incorrect conclusions

Small world network models of the dynamics of HIV infection

It has long been recognised that the structure of social networks plays an important role in the dynamics of disease propagation. The spread of HIV results from a complex network of social interactions and other factors related to culture, sexual behaviour, demography, geography and disease characteristics, as well as the availability, accessibility and delivery of healthcare. The small world phenomenon has recently been used for representing social network interactions. It states that, given some random connections, the degrees of separation between any two individuals within a population can be very small. In this paper we present a discrete event simulation model which uses a variant of the small world network model to represent social interactions and the sexual transmission of HIV within a population. We use the model to demonstrate the importance of the choice of topology and initial distribution of infection, and capture the direct and non-linear relationship between the probability of a casual partnership (small world randomness parameter) and the spread of HIV. Finally, we illustrate the use of our model for the evaluation of interventions such as the promotion of safer sex and introduction of a vaccine

Small world network models of the dynamics of HIV infection

EThOS - Electronic Theses Online ServiceGBUnited Kingdo