题目: 定量化网络分析及应用的挑战：图的相似性与图的复杂性
Challenges in Quantitative Network Analysis: Graph Similarity and Graph Complexity
报告人：马蒂亚斯教授（Prof. Matthias.dehmer）
奥地利UMIT大学教授，天津市外专千人计划学者，南开大学讲座教授
时间：2016年11月10日（星期四）下午2点-4点
地点：taptap下载安装安卓南1-120
Abstract: Quantitative network analysis has been introduced as a new branch of graph theory. It puts the emphasis on using quantitative techniques such as similarity, distance or quantifying structural information of networks by using a measurent approach. In this talk, we survey some standart and novel techniques and sketch some applications thereof.
Short CV of Matthias Dehmer： Prof. Dr. habil Matthias Dehmer studied Mathematics and Computer Science at the University of Siegen. Then his research areas became discrete mathematics, information theory, data analysis, data science, big data, and machine learning. In particular, he contributed extensively to complex network analysis and information theory. Finally he became one of the pioneers to establish an Information Theory of Networks. He got a PhD in Computer Science from the Darmstadt University. From 2005 to 2008, he held several research positions at the University of Rostock (Germany), Vienna Bio Center (Austria), Vienna Technical University (Austria) and University of Coimbra (Portugal). In October 2008, he got his habilitation in Applied Discrete Mathematics from the Vienna University of Technology. Afterwards, he started in April 2009 as Professor at the UMIT (Austria). In 2010, he got the Head of the Institute of Bioinformatics and Translational Research at UMIT. In particular, Prof. Dehmer has extensively been using methods from discrete mathematics (e.g., entropy, statistical estimation, optimization) to explore the structure of complex networks. In particular, he investigated the topological entropy of graphs extensively and used these concepts in other scientific fields. Due to his headship at the UMIT, he has been involved within a network consisting of companies in Austria, Germany and USA.
欢迎感兴趣的老师和同学们参加！
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