Frontiers in Network Embedding and GCN




报告题目: Frontiers in Network Embedding and GCN

摘要: Nowadays, larger and larger, more and more sophisticated networks are used in more and more applications. It is well recognized that network data is sophisticated and challenging. To process graph data effectively, the first critical challenge is network data representation, that is, how to represent networks properly so that advanced analytic tasks, such as pattern discovery, analysis and prediction, can be conducted efficiently in both time and space. In this talk, I will introduce the recent trends and latest progress on network embedding and GCN, including signed GNN, GNN with stochastic message passing as well as item2item graph learning for recommendation.

简介: 崔鹏,清华大学计算机系长聘副教授,博士生导师。研究兴趣聚焦于大数据驱动的因果推理和稳定预测、大规模网络表征学习等。在数据挖掘及人工智能领域顶级国际会议发表论文100余篇,先后5次获得顶级国际会议或期刊论文奖,并先后两次入选数据挖掘领域顶级国际会议KDD最佳论文专刊,包括中国入选的首篇。担任IEEE TKDE、ACM TOMM、ACM TIST、IEEE TBD等国际顶级期刊编委。曾获得国家自然科学二等奖、教育部自然科学一等奖、电子学会自然科学一等奖、北京市科技进步一等奖、中国计算机学会青年科学家奖、国际计算机协会(ACM)杰出科学家。入选中组部万人计划,并当选中国科协全国委员会委员。




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