个人简介
方一向博士，毕业于香港大学，现为澳大利亚新南威尔士大学林学民教授（IEEE Fellow）团队的全职博士后，主要从事数据库理论、图数据、时空数据和Web数据的查询与挖掘相关研究工作。截止2019年9月，方一向博士已经在数据库和数据挖掘领域国际期刊/会议（如VLDBJ、TKDE、VLDB、ICDE等）发表论文35篇，其中包括21篇中国计算机学会认定的A类论文（即CCF-A），以第一作者身份发表的10篇CCF-A长文，以第一作者身份发表的4篇CCF-A短文，和以通讯作者身份发表的4篇CCF-A论文。方一向博士还发表了中国计算机学会认定的B类论文（即CCF-B）6篇长文，包括以第一作者身份发表的1篇长文（KAIS 1篇）。方一向博士多次受邀在国内外学术机构和学术会议做学术报告，并常年受邀担任国际数据库领域多个高水平学术会议（如ICDE、CIKM、ASONAM、WISE等）的程序委员会成员和顶级期刊（如VLDBJ、TKDE、IS、KAIS等）的审稿人。方一向博士还担任国际数据库领域知名期刊Information Processing and Management（CCF-B类期刊）编辑委员会的编委（Editorial Board Member）。方一向博士还作为骨干成员积极参与了香港研究资助局RGC、澳大利亚研究委员会ARC的多个科研项目。个人主页：
http://www.cse.unsw.edu.au/~z3525370/
报告摘要
With the rapid development of social media, online communities, mobile communications, huge volumes of digital data are accumulated with data objects often involving complex relationships. Consequently, the accumulated data are usually modelled as graphs, where objects are represented by vertices and relationships are represented by edges. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications but also brings great challenges in computation. In this talk, I will focus on an important research topic, called cohesive subgraph search over large graphs. In particular, I will discuss two sub-topics, namely community search and densest subgraph discovery. The first one aims to efficiently search the community of a query vertex over large graphs, while the second one computes the subgraph whose density is the highest. Both efficient algorithms and experimental results will be discussed. In addition, a system prototype will be presented.
