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Graph kernels can be intuitively understood as functions measuring the similarity of pairs of graphs. They allow [[Kernel trick|kernelized]] learning algorithms such as [[support vector machine]]s to work directly on graphs, without having to do [[feature extraction]] to transform them to fixed-length, real-valued [[feature vector]]s. They find applications in [[bioinformatics]], in [[chemoinformatics]] (as a type of [[molecule kernel]]s<ref name="Ralaivola2005">{{cite journal |author1=L. Ralaivola |author2=S. J. Swamidass |author3=H. Saigo |author4=P. Baldi |title=Graph kernels for chemical informatics |journal=Neural Networks |year=2005 |volume=18 |issue=8 |pages=1093–1110 |doi=10.1016/j.neunet.2005.07.009|pmid=16157471 }}</ref>), and in [[social network analysis]].<ref name="Vishwanathan"/>
Concepts of graph kernels have been around since the 1999, when D. Haussler<ref>{{Cite book|title=Convolution Kernels on Discrete Structures|last=Haussler|first=David|date=1999|citeseerx = 10.1.1.110.638}}</ref> introduced convolutional kernels on discrete structures. The term graph kernels was more officially coined in 2002 by R. I. Kondor and [[John D. Lafferty|J. Lafferty]]<ref>{{cite conference
|title=Diffusion Kernels on Graphs and Other Discrete Input Spaces
|author1=Risi Imre Kondor
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