報(bào)告題目：Searching Cohesive Groups in Big Graph Data

報(bào)告時(shí)間：2024年7月4日 9:00-11:00

報(bào)告地點(diǎn)：電航樓218會(huì)議室

報(bào)告摘要：The availability of big attributed or heterogeneous graph data brings great opportunities for realizing big values of data. Making sense of such big graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. Most of the previous studies have focused on finding cohesive subgraphs from a graph without considering attributes or heterogeneity. As such, the returned cohesive may miss out important information describing a variety of features of real applications. Recently, cohesive group search in heterogeneous information networks (HINs) has begun to attract attention. In this talk, I will introduce several works of our group for modelling and searching cohesive groups in various large-scale information networks.

報(bào)告人簡(jiǎn)介：Chengfei Liu is a Professor and the head of Web and Data Engineering research group in the Department of Computing Technologies at Swinburne University of Technology, Australia. He received the BS, MS and PhD degrees in Computer Science from Nanjing University, China in 1983, 1985 and 1988, respectively. Prior to joining Swinburne, he taught at the University of South Australia and the University of Technology Sydney, and was a Senior Research Scientist at Cooperative Research Centre for Distributed Systems Technology (DSTC) located at University of Queensland. He also held visiting positions at the Chinese University of Hong Kong, the University of Aizu in Japan, and IBM Silicon Valley Lab in USA. He has attracted over 10 million dollars in research grants (including 16 grants awarded by the Australia Research Council), published close to 300 papers in prestigious journals and conferences, and served in over 130 organization committees and program committees. His current research interests include graph data management over large networks, heterogeneous information networks, keyword search on structured data, query processing and refinement for advanced database applications.

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信息科學(xué)技術(shù)學(xué)院

2024年7月1日