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Local2Global : a distributed approach for scaling representation learning on graphs
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Jeub, Lucas G. S., Colavizza, Giovanni, Dong, Xiaowen, Bazzi, Marya and Cucuringu, Mihai (2023) Local2Global : a distributed approach for scaling representation learning on graphs. Machine Learning, 112 . pp. 1663-1692. doi:10.1007/s10994-022-06285-7 ISSN 2632-2153.
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Official URL: http://doi.org/10.1007/s10994-022-06285-7
Abstract
We propose a decentralised “local2global” approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or “patches”) and training local representations for each patch independently. In a second step, we combine the local representations into a globally consistent representation by estimating the set of rigid motions that best align the local representations using information from the patch overlaps, via group synchronization. A key distinguishing feature of local2global relative to existing work is that patches are trained independently without the need for the often costly parameter synchronization during distributed training. This allows local2global to scale to large-scale industrial applications, where the input graph may not even fit into memory and may be stored in a distributed manner. We apply local2global on data sets of different sizes and show that our approach achieves a good trade-off between scale and accuracy on edge reconstruction and semi-supervised classification. We also consider the downstream task of anomaly detection and show how one can use local2global to highlight anomalies in cybersecurity networks.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | ||||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Neural networks (Computer science), Graph theory -- Data processing, Synchronization | ||||||||
Journal or Publication Title: | Machine Learning | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 2632-2153 | ||||||||
Official Date: | May 2023 | ||||||||
Dates: |
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Volume: | 112 | ||||||||
Page Range: | pp. 1663-1692 | ||||||||
DOI: | 10.1007/s10994-022-06285-7 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 21 March 2023 | ||||||||
Date of first compliant Open Access: | 21 March 2023 | ||||||||
RIOXX Funder/Project Grant: |
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