Ge, Y., Peng, P. and Lu, H. orcid.org/0000-0002-0349-2181 (2021) Mixed-order spectral clustering for complex networks. Pattern Recognition, 117. 107964. ISSN 0031-3203
Abstract
Spectral clustering (SC) is a popular approach for gaining insights from complex networks. Conventional SC focuses on second-order structures (e.g. edges) without direct consideration of higher-order structures (e.g. triangles). This has motivated SC extensions that directly consider higher-order structures. However, both approaches are limited to considering a single order. To address this issue, this paper proposes a novel Mixed-Order Spectral Clustering (MOSC) framework to model both second-order and third-order structures simultaneously. To model mixed-order structures, we propose two new methods based on Graph Laplacian (GL) and Random Walks (RW). MOSC-GL combines edge and triangle adjacency matrices, with theoretical performance guarantee. MOSCRW combines first-order and second-order random walks for a probabilistic interpretation. Moreover, we design mixed-order cut criteria to enable existing SC methods to preserve mixed-order structures, and develop new mixed-order evaluation metrics for structure-level evaluation. Experiments on community detection and superpixel segmentation show 1) the superior performance of the MOSC methods over existing SC methods, 2) enhanced performance of conventional SC due to mixed-order cut criteria, and 3) new insights of output clusters offered by the mixed-order evaluation metrics.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier B.V. This is an author produced version of a paper subsequently published in Pattern Recognition. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Spectral clustering; higher-order structures; mixed-order structures |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Apr 2021 09:10 |
Last Modified: | 06 Apr 2022 00:38 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.patcog.2021.107964 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172646 |