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Title: The journey of graph kernels through two decades
Authors: Ghosh, Swarnendu
Das, Nibaran
Gonçalves, Teresa
Quaresma, Paulo
Kundu, Mahantapas
Issue Date: 2018
Publisher: Elsevier
Citation: Swarnendu Ghosh, Nibaran Das, Teresa Gonçalves, Paulo Quaresma, and Mahantapas Kundu. The journey of graph kernels through two decades. Computer Science Review, 27:88 – 111, 2018.
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.
Type: article
Appears in Collections:INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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