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Poster
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
Saurabh Verma · Zhi-Li Zhang

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #16 #None

For the purpose of learning on graphs, we hunt for a graph feature representation that exhibit certain uniqueness, stability and sparsity properties while also being amenable to fast computation. This leads to the discovery of family of graph spectral distances (denoted as FGSD) and their based graph feature representations, which we prove to possess most of these desired properties. To both evaluate the quality of graph features produced by FGSD and demonstrate their utility, we apply them to the graph classification problem. Through extensive experiments, we show that a simple SVM based classification algorithm, driven with our powerful FGSD based graph features, significantly outperforms all the more sophisticated state-of-art algorithms on the unlabeled node datasets in terms of both accuracy and speed; it also yields very competitive results on the labeled datasets - despite the fact it does not utilize any node label information.

Author Information

Saurabh Verma (University of Minnesota Twin Cities)

I am a PhD candidate (Fall 2015 - Present) at the Department of Computer Science, University of Minnesota Twin Cities. My current research focuses on developing Deep Convolutional Networks Models for Graphs. In the past, I have worked on research problems related to learning from high dimensional data, manifold learning, semi-supervised learning in natural language processing, optimization and anomaly detection. My [Webpage](http://www-users.cs.umn.edu/~verma/) and [Blog.](http://vermamachinelearning.github.io/archive.html)

Zhi-Li Zhang (University of Minnesota)

Professor Zhang is McKnight Distinguished University Professor and Qwest Chair Professor at Department of Computer Science & Engineering, University of Minnesota. He received his B.S. degree in Computer Science from Nanjing University, China, and his M.S. and Ph.D. degrees in Computer Science from the University of Massachusetts, Amherst. Dr. Zhang’s research interests lie broadly in computer communication and networks, Internet technology, multimedia and emerging applications. His past research was centered on the analysis, design and development of scalable Internet QoS solutions to support performance-demanding multimedia applications. His current research focuses on building highly scalable, resilient and secure Internet and cyber-physical systems & infrastructures and developing mechanisms to enhance Internet service availability, reliability and security, and on developing next generation, service-oriented, manageable Internet architectures to provide better support for creation, deployment, operations and management of value-added smart services and underlying networks, including mobile, cloud and content delivery services and networks. Dr. Zhang has published more than 150 journal, conference and workshop papers. Dr. Zhang has received several honors for his research, and he is co-recipient of a number of Best Paper Awards. He is a Fellow of IEEE.

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