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4月22日交叉学科论坛学术报告(Dr Shirui Pan, University of Technology Sydn
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发布时间:2018-04-19 11:48
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报告题目: Complex Network Representation Learning and Graph Classification
报告人: Dr Shirui Pan
报告地点: 计算机学院B403
报告时间: 2018年4月22日上午9:00(周日)
报告人单位: University of Technology Sydney
报告人简介:
Shirui Pan received the Ph.D. degree in computer science in 2015 from the University of Technology Sydney (UTS), Sydney, Australia. He is a Research Fellow with the Centre for Artificial Intelligence at UTS. He has published over 50 research papers in top-tier journals and conferences. In particular, 12 articles are published in IEEE Transactions series, including IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Knowledge and Data Engineering (TKDE), the IEEE Transactions on Cybernetics (TCYB). Other papers are published in ICDE, IJCAI, ICDM, SDM, CIKM, and PAKDD. He is an Associate Editor of the journal IEEE Access (IF: 3.244) and the guest editor of the journal Complexity (IF: 4.621). He also regularly serves as a Program Committee Member for a number of conferences, including IJCAI, AAAI, and PAKDD. His current research interests include data mining and artificial intelligence.
报告摘要: Networks or graphs are powerful data representation tools in many real-life applications. In this talk, I will introduce my recent research on analyzing a single network and a graph dataset: network representation learning and graph classification. In analyzing a single large network, network representation or embedding, which converts each node in a single graph into a low dimensional space, is an effective approach for many network analytics problems. In analyzing a graph dataset, discriminative subgraph selection based approaches provide not only high performance in graph classification but also interpretation ability. Both solutions for network representation and graph classification are presented and their real-world applications are discussed.
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- 上一篇:2018年4月18日学术报告(黄如花,武汉大学珞珈特聘教授)
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