日期：2003年3月27日 16:48:00

Shlomo Havlin教授的学术报告时间和地点安排如下: 1. 3月10日下午2:30-5:00，办公楼第一会议室，作题为: 《Novel Percolation Theory in Complex Networks: Application to Internet， Protein Networks and Social Networks（新的 复杂网络逾渗理论在因特网、蛋白质及社会网络中的应用) 》的学术报告。 ABSTRACT: It is found that many networks in the world such as telephone, flight, communication, social and Internet networks do not behave like regular networks where each node is connected via links to a typical number of nodes. Instead, in such networks, which are called, scale free networks, there is no characteristic number of links per node, but a very broad distribution of links exists. It is shown that many physical properties become anomalous on such networks. In particular, we study, using percolation theory, the stability of of such networks under both random breakdown of nodes and intentional attack on the highest connected nodes. We focus on scale free networks, such as the Internet, and show that it is resilient to random breakdown [1], but very sensitive to intentional attack [2]. We also describe the behavior of the network near the percolation phase transition and show that the critical exponents are influenced by the scale free nature of the network. References: [1] Resilience of the Internet to random breakdown R. Cohen, K. Erez, D. ben-Avraham and S. Havlin Phys. Rev. Lett. 85, 4626 (2000) [2] Breakdown of the Internet under intentional attack R. Cohen, K. Erez, D. ben-Avraham and S. Havlin Phys. Rev. Lett. 86, 3682 (2001) 2. 3月11日下午3:00-5:00，力学一楼五层十三系会议室（504）,作题为: 《Scaling Approach to the Question of Global Warming (利用标度律方法研究全球变暖问 题)》的学术报告。 ABSTRACT: We test the scaling performance of seven leading global climate models by using detrended fluctuation analysis. We analyse temperature records of six representative sites around the globe simulated by the models, for two different scenarios: (i) with greenhouse gas forcing only and (ii) with greenhouse gas plus aerosol forcing. We find that the simulated records for both scenarios fail to reproduce the universal scaling behavior of the observed records, and display wide performance differences. The deviations from the scaling behavior are more pronounced in the first scenario, where also the trends are clearly overestimated. Since the models underestimate the long-range persistence of the atmosphere and overestimate the trends, our analysis suggests that the anticipated global warming is also overestimated by the models.