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Deep Renyi entropy graph kernel
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DOI：10.1016/j.patcog.2020.107668.png)
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摘要
En 中文
Graph kernels are applied heavily for the classification of structured data. In this paper, we propose a deep Renyi entropy graph kernel for this purpose. We gauge the deep information through a family of h-layer expansion subgraphs rooted at a vertex, and define a h-layer depth-based second-order Renyi entropy representation for each vertex. The second-order Renyi entropy representation is used together with Euclidean distance to build a deep second-order Renyi entropy graph kernel (SREGK). For graphs with n vertices, the time complexity for our kernel is O(n(3)). This low-order polynomial complexity enables our subgraph kernels to easily scale up to graphs of reasonably large sizes and thus overcome the size limits arising in state-of-the-art graph kernels. Experimental results on fourteen real world graph datasets are shown to demonstrate the overall superior performance of our approach over a number of state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Shannon entropy
Renyi entropy
Deep representation
Graph kernel
Graph classification
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期刊
IF:
7.6
论文数:
1.4W
被引数:
4.5W
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