Bai, Lu and Hancock, Edwin R orcid.org/0000-0003-4496-2028 (2016) Fast depth-based subgraph kernels for unattributed graphs. Pattern Recognition. pp. 233-245. ISSN 0031-3203
Abstract
In this paper, we investigate two fast subgraph kernels based on a depth-based representation of graph-structure. Both methods gauge depth information through a family of K-layer expansion subgraphs rooted at a vertex [1]. The first method commences by computing a centroid-based complexity trace for each graph, using a depth-based representation rooted at the centroid vertex that has minimum shortest path length variance to the remaining vertices [2]. This subgraph kernel is computed by measuring the Jensen-Shannon divergence between centroid-based complexity entropy traces. The second method, on the other hand, computes a depth-based representation around each vertex in turn. The corresponding subgraph kernel is computed using isomorphisms tests to compare the depth-based representation rooted at each vertex in turn. For graphs with n vertices, the time complexities for the two new kernels are O(n 2) and O(n 3), in contrast to O(n 6) for the classic Gärtner graph kernel [3]. Key to achieving this efficiency is that we compute the required Shannon entropy of the random walk for our kernels with O(n 2) operations. This computational strategy 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. Experiments on standard bioinformatics and computer vision graph datasets demonstrate the effectiveness and efficiency of our new subgraph kernels.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Elsevier. This is an author produced version of a paper published in Pattern Recognition. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Depth-based representations,Entropy,Graph isomorphism tests,Graph kernels,The Jensen-Shannon divergence |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 01 Dec 2015 13:55 |
Last Modified: | 16 Oct 2024 12:39 |
Published Version: | https://doi.org/10.1016/j.patcog.2015.08.006 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.patcog.2015.08.006 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:92418 |