Abstract
Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local (“microscopic”) level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level – fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks.
Highlights
Many natural and man-made complex systems such as biological networks, the WWW, airport network and stock markets network, evolve intensively at the local level [1,2,3]
Five thousand scientists are sampled from the AAMC Faculty Roster according to the criteria that their academic life spans are longer than 10 years and each of them has more than 10 collaborators
We argue that when elaborating a model of the evolving scientific collaboration network and other social networks, except for considering the growing mechanism based on existing topology [14,18] and modifying connections as a feedback of the dynamical process on the network [8], future study should take the ability of nodes attracting connections and the life spans of links as intrinsic properties embedded in the systems
Summary
Many natural and man-made complex systems such as biological networks, the WWW, airport network and stock markets network, evolve intensively at the local level [1,2,3]. There are several notable models of network evolution including the preferential attachment model [7] and the adaptive network models in which network topology evolves as a feedback to the state change of nodes [8]. The scientific collaboration network, which bears the same statistical properties as many stationary complex networks [13], has been shown, in numerical simulations, to be vulnerable to targeted removal of important nodes [9]. Exactly how the intensively evolving scientific collaboration network responds to such attacks in the real world has not been carefully studied
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