报告人：瑞士弗里堡大学 Matus Medo

报告时间：2018年7月7日上午11:05

报告地点：淮安神旺大酒店会议报告厅

报告摘要：Complex networks theory is a rapidly developing interdisciplinary research field. In this talk, we will focus on growing networks where time plays a crucial role, and which are relevant in diverse fields such as bibliometrics (to model citations among scientific papers) and e-commerce (to model users and their purchases on Amazon.com, for example). We will show that usual network metrics and algorithms that ignore the time information produce inferior or even misleading results. To resolve this problem, we will introduce time-aware methods for classical problems such as ranking the network nodes and community detection.（复杂网络理论是一个快速发展的跨学科研究领域。在这次演讲中，我们将聚焦不断增长的网络，在这些网络中，时间扮演着至关重要的角色，与不同的领域相关，比如文献计量学(对科学论文的引用进行建模)和电子商务(例如，对用户及其在亚马逊网站上的购买行为进行建模)。通常的网络度量和算法由于忽略了时间信息，将导致劣质甚至是误导性的结果。为了解决这一问题，我们介绍一些经典问题的时间感知方法，如对网络节点进行排序和社区检测。）

报告人简介：Data scientist at the Department of Clinical Research, University of Bern. Lecturer at the Department of Physics, University of Fribourg. Research fellow at the Institute of Fundamental and Frontier Sciences, UESTC Chengdu. Research interests: complex networks, information filtering, data analysis. Published multiple research papers on top journal, PRL, PNAS, Physics Report etc, Received over 2000 citations. （伯尔尼大学临床研究系数据科学家。 弗里堡大学物理系讲师。 成都电子科技大学基础与前沿科学研究所研究员。 研究兴趣：复杂网络，信息过滤，数据分析。 在PRL，PNAS，Physics Report等顶级期刊发表多篇研究论文，总引用超过2000次。）