Abstract
A novel kernel-based support vector machine (SVM) for graph classification is proposed. The SVM feature space mapping consists of a sequence of graph convolutional layers, which generates a vector space representation for each vertex, followed by a pooling layer which generates a reproducing kernel Hilbert space (RKHS) representation for the graph. The use of a RKHS offers the ability to implicitly operate in this space using a kernel function without the computational complexity of explicitly mapping into it. The proposed model is trained in a supervised end-to-end manner whereby the convolutional layers, the kernel function and SVM parameters are jointly optimized with respect to a regularized classification loss. This approach is distinct from existing kernel-based graph classification models which instead either use feature engineering or unsupervised learning to define the kernel function. Experimental results demonstrate that the proposed model outperforms existing deep learning baseline models on a number of datasets.
Highlights
The world contains much implicit structure which can be modelled using a graph
Advances in the application of deep learning or neural networks to sequence spaces in the context of natural language processing and fixed dimensional vector spaces in the context of computer vision has led to much interest in applying deep learning to graphs
In this work we focus on the task of graph classification
Summary
An image can be modelled as a graph where objects (e.g. person, chair) are modelled as vertices and their pairwise relationships (e.g. sitting) are modelled as edges (Krishna et al 2017) This representation has led to useful solutions for many vision problems including image captioning and visual question answering (Chen et al 2019). A street network can be modelled as a graph where locations are modelled as vertices and street segments are modelled as edges This representation has led to useful solutions for many transportation problems including the placement of electrical vehicle charging stations (Gagarin and Corcoran 2018). There exist many types of machine learning tasks one may wish to perform on graphs These (2020) 5:39 include vertex classification, graph classification, graph generation (You et al 2018) and learning implicit/hidden structures (Franceschi et al 2019). Examples of graph classification tasks include human activity recognition where human pose is modelled using a skeleton graph (Yan et al 2018), visual scene understanding where the scene is modelled using a scene graph (Xu et al 2017) and semantic segmentation of three dimensional point clouds where the point cloud is modelled as a graph of geometrically homogeneous elements (Landrieu and Simonovsky 2018)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
