Network Representation Learning: Opportunities and Open Issues

主讲人 : Ling Liu

Mining information networks have traditionally relied on observable features, such as node and   properties as well as user-defined statistical features extracted from complex networks, such as node degree, traversal path.  With the recent success of deep neural networks, a wide variety of deep neural network models have been proposed, which can automatically learn to encode network structure into low-dimensional  dings, using techniques  d on deep learning and nonlinear dimensionality reduction. These network representation learning (NRL) approaches replace the need for manual feature engineering with automated learning of latent features of network representation, and have led to state-of-the-art results in network- d tasks, such as node classification, node clustering, and   prediction. In this talk, we will describe the main recent advancements in NRL, including network  ding, graph neural networks. We will discuss methods to   individual nodes as well as algorithms to   entire (sub)graphs. Finally, we will present a unified  work for NRL. Most existing models learn node  dings through flat information propagation across the edges within each node’s local neighborhood. We propose a general  work for graph neural networks to learn node representations. Our approach generates node  dings that preserve the global structure of the original graphs at different levels of the graph hierarchy.

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主讲人简介：

Prof. Dr. Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large-scale data intensive systems. Prof. Liu is an internationally recognized expert in the areas of Big Data Systems and Analytics, Distributed Systems, Data  and Storage Systems, Internet Computing, Privacy, Security and Trust. Prof. Liu has published over 300 international journal and conference articles, and is a recipient of the best paper award from a number of top venues, including ICDCS 2003, WWW 2004, 2005 Pat Goldberg Memorial Best Paper Award, IEEE CLOUD 2012, IEEE ICWS 2013, ACM/IEEE CCGrid 2015, IEEE Edge 2017. Prof. Liu is an elected IEEE Fellow and a recipient of IEEE Computer Society Technical Achievement Award. Prof. Liu has served as general chair and PC chairs of numerous IEEE and ACM conferences in the fields of big data, cloud computing, data engineering, distributed computing, very large data s, World Wide Web, and served as the editor in chief of IEEE Transactions on Services Computing from 2013-2016. Currently Prof. Liu is co-PC chair of The Web 2019 (WWW 2019) and the Editor in Chief of ACM Transactions on Internet Technology (TOIT). Prof. Liu’s research is primarily sponsored by NSF, IBM and Intel.