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SSSNET: semi-supervised signed network clustering

Abstract:

Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited. In particular, most state-of-the-art methods generating node embeddings of signed networks focus on link sign prediction, and those that pertain to node clustering are usually not graph neural network (GNN) methods. Here, we introduce a novel probabilistic balanced normalized cut loss for training nodes in a GNN framework for semi-s...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1137/1.9781611977172.28

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Merton College
Role:
Author
ORCID:
0000-0002-8464-2152
Publisher:
Society for Industrial and Applied Mathematics
Host title:
Proceedings of the SIAM International Conference on Data Mining (SDM22)
Pages:
244-252
Publication date:
2022-04-20
Acceptance date:
2021-12-19
Event title:
SIAM International Conference on Data Mining (SDM22)
Event location:
Alexandria, VA, USA
Event website:
https://www.siam.org/conferences/cm/submissions-and-deadlines/sdm22-submissions-deadlines
Event start date:
2022-04-28
Event end date:
2022-04-30
DOI:
EISBN:
978-611977172
Language:
English
Keywords:
Pubs id:
1232881
Local pid:
pubs:1232881
Deposit date:
2022-01-18

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