Tree-Based Kernel for Graphs With Continuous Attributes
By Giovanni Da San Martino, Nicoló Navarin, Alessandro Sperduti. 2015
28 citations recorded by the source corpus.
The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing Kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to nondiscrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this brief, we propose a graph kernel for complex and continuous nodes’ attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of the state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel, which reduces its complexity significantly. Experimental results obtained on six real-world data sets show that the kernel is the best performing one on most of them. Moreover, in most cases, the approximated version reaches comparable performances to the current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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Citations in the corpus
- Pre-training Graph Neural Networks with Kernels (2018, neutral): “sits, i.e. the Ordered Decomposition DAGs kernels, has been proposed in Da San Martino et al. [2012b, 2016]. The framework has also been extended to consider graphs with continuous attributes [Da San Martino et al., 2017]. We do not consider continuous attributes in this work, however it is one of i”
- RetGK: Graph Kernels based on Return Probabilities of Random Walks (2018, background): “Weisfeiler-Lehman graph kernels [25] and tree-based kernels [6] are developed with subtree patterns.”