Le Song, Associate Director, Center for Machine Learning, Georgia Institute of Technology, and Alibaba
Networks and graphs are prevalent in many real world applications such as online social networks, transactions in payment platforms, user-item interactions in recommendation systems and relational information in knowledge bases. The availability of large amount of graph data has posed great new challenges. How to represent such data to capture similarities or differences between involved entities? How to learn predictive models and perform reasoning based on a large amount of such data? Previous deep learning models, such as CNN and RNN, are designed for images and sequences, but they are not applicable to graph data.
In this talk, I will present an embedding framework, called Structure2Vec, for learning representation for graph data in an end-to-end fashion. Structure2Vec provides a unified framework for integrating information from node characteristics, edge features, heterogeneous network structures and network dynamics, and linking them to downstream supervised and unsupervised learning, and reinforcement learning. I will also discuss several applications in security analytics where Structure2Vec leads to significant improvement over previous state-of- the-arts.
Le Song, Associate Director, Center for Machine Learning, Ali Baba, and Georgia Institute of Technology
Le Song is an Associate Professor in the Department of Computational Science and Engineering, College of Computing, and an Associate Director of the Center for Machine Learning, Georgia Institute of Technology. Le is also working with Ant Financial AI Department on risk management, security and finance related problems. He received his Ph.D. in Machine Learning from University of Sydney and NICTA in 2008, and then conducted his post-doctoral research in the Department of Machine Learning, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology in 2011, he was a research scientist at Google briefly. His principal research direction is machine learning, especially nonlinear models, such as kernel methods and deep learning, and probabilistic graphical models for large scale and complex problems, arising from artificial intelligence, network analysis and other interdisciplinary domains. He is the recipient of the Recsys '16 Deep Learning Workshop Best Paper Award, AISTATS '16 Best Student Paper Award, IPDPS '15 Best Paper Award, NSF CAREER Award '14, NIPS '13 Outstanding Paper Award, and ICML '10 Best Paper Award. He has also served as the area chair or senior program committee for many leading machine learning and AI conferences such as ICML, NIPS, AISTATS, AAAI and IJCAI. He is also the action editor for JMLR, and associate editor for IEEE PAMI.
author = {Le Song},
title = {Structure2vec: Deep Learning for Security Analytics over Graphs},
year = {2018},
address = {Atlanta, GA},
publisher = {USENIX Association},
month = may
}