The journey of graph kernels through two decades |
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Affiliation: | 1. Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, 33720 Tampere, Finland;2. Department of Computer Science, Universität der Bundeswehr München, 85577 Neubiberg, Germany;3. Center for Combinatorics and LPMC-TJKLC, Nankai University, Tianjin, PR China;1. School of Computer & Software, Nanjing University of Information Science & Technology, Jiangsu, Nanjing 210-044, China;2. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210-044, China;3. School of Economic & Management, Nanjing University of Information Science & Technology, Nanjing 210044, China |
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Abstract: | In the real world all events are connected. There is a hidden network of dependencies that governs behavior of natural processes. Without much argument it can be said that, of all the known data-structures, graphs are naturally suitable to model such information. But to learn to use graph data structure is a tedious job as most operations on graphs are computationally expensive, so exploring fast machine learning techniques for graph data has been an active area of research and a family of algorithms called kernel based approaches has been famous among researchers of the machine learning domain. With the help of support vector machines, kernel based methods work very well for learning with Gaussian processes. In this survey we will explore various kernels that operate on graph representations. Starting from the basics of kernel based learning we will travel through the history of graph kernels from its first appearance to discussion of current state of the art techniques in practice. |
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Keywords: | Graph kernels Support vector machines Graph similarity Isomorphism |
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