Abstract: Feature engineering is one of the most important parts of machine learning task. As a most universal format of data existence, such as social network, information network etc, information networks have been widely studied within a broad scope of machine learning. Network Representation Learning (NRL) aims to project network nodes (or other entities) into a low-dimensional vector space while preserving general proximity between nodes, and has become a hot topic in data mining and machine learning. In this talk, I will first overview the basic concepts of NRL, and give an introduction about our recent advancements in such field, such as tag embedding, domain adaptive network embedding, and landmark based network embedding etc.
Biography：Guojie Song is an associate professor in the School of Electronics Engineering and Computer Science at Peking University. His research interests mainly include data mining and machine learning on network data, such as social network analysis, transportation network analysis etc. He has published 100+ papers, and more than 50+ paper published at leading journals and conferences, including TKDE, TPDS, TITS, TKDD, AAAI, IJCAI, CIKM, ICDM etc. He served as PC member and Senior PC member of AI related top conferences. His research results have received three awards from ministry and provinces in China, such as the first prize of Science and Technology award of China Highway Society (2012, 2013) and the Second Prize of Shanxi Science and Technology Award (2012).