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Cohesive structure based complex network analytics: from motif counting to graph hierarchical decomposition
浏览次数:日期:2020-09-24编辑:本科教学办

报告人:澳大利亚南新南威尔士大学 王凯副教授

报告时间:2020915日上午9:00 -10:00

报告地点:腾讯会议(会议ID286438288

报告人简介:

Kai Wang is currently a Postdoc Researcher in the Data and Knowledge Research Group at The University of New South Wales. He obtained his Ph.D. from The University of New South Wales in 2020 and his bachelor’s degree from Zhejiang University in 2016, both in Computer Science. In 2015, He worked as a research assistant at The Hong Kong Polytechnic University. His research interests lie in big data analytics, especially for the graph/network and spatial data. He has published several papers in top conferences, including VLDB and ICDE.

报告摘要:

Cohesive structures widely exist in networks and mining cohesive structures is one of the most fundamental problems to analyze complex networks. In this presentation, different levels of cohesive-structure-based network analytic methods are introduced to meet the demand of nowadays real-world applications. Firstly, in the motif level, the butterfly (i.e., a complete 2 x 2 biclique) counting problem on bipartite networks is investigated.  Secondly, in the subgraph mining level, the radius-bounded k-cores (RB-k-cores) computing problem on geo-social networks which aims to find cohesive subgraphs satisfying both social and spatial constraints is introduced. Thirdly, in the graph hierarchical decomposition level, the efficient bitruss decomposition algorithms on bipartite networks is presented.