學術講座：Investigating Dynamic Networks
應伟德国际1946官方网的邀請，英國倫敦瑪麗女王大學（QueenMary, University of London）理查德·克萊格（Richard Clegg）博士将為廣大師生作題為《Investigating dynamic networks》的學術報告。
講座題目:Investigatingdynamic networks
主講人: Dr.Richard Clegg ——英國倫敦瑪麗女王大學
時間: 2017年12月21日（周四）晚上18:00 -- 19:00
地點:西土城校區教三樓3-435
講座内容:動态網絡主要研究互連的網絡節點随時間變化的特點和規律。凡是互相連接的對象集合均可視為網絡：城市道路（通過街道連接的交通網絡）、社會網絡（例如Wechat，通過在線交互形成的個人網絡）、因特網（通過電子鍊路構建的計算機網絡）。
本講座首先介紹各種網絡之間的結構相似性（如朋友圈與大規模因特網在數學描述／含義上，具有類似的結構），然後讨論偏好連接（preferential attachment）法則，及基于該假設的網絡增長／變化模型。偏好連接法則隻是真實世界網絡的一種近似，因此Richard Clegg博士将重點闡述其研究成果FETA（Framework for Evolving Topology Analysis,演進拓撲分析框架）在動态網絡描述和分析方面的性能。
該講座為前沿講座，歡迎全校師生踴躍參加。
伟德国际1946官方网
2017年12月19日
附：
Short Bio of Dr. RichardClegg:
Richard G. Clegg is a Lecturer in Networks at Queen MaryUniversity of London. His PhD in mathematics and statistics from theUniversity of York was gained in 2005. His research interests include investigationsof the dynamic behaviour of networks and measurement of network trafficstatistics.
Brief introduction ofthis talk:
In this short talk I will introduce thestudy of dynamic networks, how networks of connected items evolve as time goeson. The networks can be any collection of connected objects. A city's roads canbe seen as a network of junctions connected by streets; A group of people canbe seen as individuals connected by friendships; A social network like Wechatcan be seen as individuals connected by their online interactions; The Internetcan be seen as a group of computer networks connected by electronic links.
I will show how many very different typesof networks can be shown to have a similar structure. A network of friends hasa similar structure mathematically to the large scale Internet. A law known as preferentialattachment has been hypothesised to cover how such networks might grow.However, this law is only approximate for the study of real world networks. Iwill show how my work on FETA (Framework for Evolving Topology Analysis)provides a way to introduce a flexible group of models of networks and also howit provides a reliable method to show which model is best to explain a givennetwork.
