Cleora: A Simple, Strong and Scalable Graph Embedding Scheme

Barbara Rychalska; Piotr Bąbel; Konrad Gołuchowski; Andrzej Michałowski; Jacek Dąbrowski; Przemysław Biecek

doi:10.1007/978-3-030-92273-3_28

Cleora: A Simple, Strong and Scalable Graph Embedding Scheme

Barbara Rychalska, Piotr Bąbel, Konrad Gołuchowski, Andrzej Michałowski, Jacek Dąbrowski, Przemysław Biecek

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The area of graph embeddings is currently dominated by contrastive learning methods, which demand formulation of an explicit objective function and sampling of positive and negative examples. One of the leading class of models are graph convolutional networks (GCNs), which suffer from numerous performance issues. In this paper we present Cleora: a purely unsupervised and highly scalable graph embedding scheme. Cleora can be likened to a GCN stripped down to its most effective core operation - the repeated neighborhood aggregation. Cleora does not require the application of a GPU and can embed massive graphs on CPU only, beating other state-of-the-art CPU algorithms in terms of speed and quality as measured on downstream tasks. Cleora has been applied in top machine learning competitions involving recommendations and graph processing, taking the podium in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We open-source Cleora under the MIT license allowing commercial use under https://github.com/Synerise/cleora.

Original language	English
Title of host publication	Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
Editors	Teddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	338-352
Number of pages	15
ISBN (Print)	9783030922726
DOIs	https://doi.org/10.1007/978-3-030-92273-3_28
Publication status	Published - 2021
Externally published	Yes
Event	28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online Duration: 8 Dec 2021 → 12 Dec 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13111 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th International Conference on Neural Information Processing, ICONIP 2021
City	Virtual, Online
Period	8/12/21 → 12/12/21

Keywords

Graph convolutional networks
Graph embedding
Node embedding

Access to Document

10.1007/978-3-030-92273-3_28

Cite this

Rychalska, B., Bąbel, P., Gołuchowski, K., Michałowski, A., Dąbrowski, J., & Biecek, P. (2021). Cleora: A Simple, Strong and Scalable Graph Embedding Scheme. In T. Mantoro, M. Lee, M. A. Ayu, K. W. Wong, & A. N. Hidayanto (Eds.), Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings (pp. 338-352). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13111 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-92273-3_28

Rychalska, Barbara ; Bąbel, Piotr ; Gołuchowski, Konrad et al. / Cleora : A Simple, Strong and Scalable Graph Embedding Scheme. Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. editor / Teddy Mantoro ; Minho Lee ; Media Anugerah Ayu ; Kok Wai Wong ; Achmad Nizar Hidayanto. Springer Science and Business Media Deutschland GmbH, 2021. pp. 338-352 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The area of graph embeddings is currently dominated by contrastive learning methods, which demand formulation of an explicit objective function and sampling of positive and negative examples. One of the leading class of models are graph convolutional networks (GCNs), which suffer from numerous performance issues. In this paper we present Cleora: a purely unsupervised and highly scalable graph embedding scheme. Cleora can be likened to a GCN stripped down to its most effective core operation - the repeated neighborhood aggregation. Cleora does not require the application of a GPU and can embed massive graphs on CPU only, beating other state-of-the-art CPU algorithms in terms of speed and quality as measured on downstream tasks. Cleora has been applied in top machine learning competitions involving recommendations and graph processing, taking the podium in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We open-source Cleora under the MIT license allowing commercial use under https://github.com/Synerise/cleora.",

keywords = "Graph convolutional networks, Graph embedding, Node embedding",

author = "Barbara Rychalska and Piotr B{\c a}bel and Konrad Go{\l}uchowski and Andrzej Micha{\l}owski and Jacek D{\c a}browski and Przemys{\l}aw Biecek",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",

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doi = "10.1007/978-3-030-92273-3_28",

language = "English",

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editor = "Teddy Mantoro and Minho Lee and Ayu, {Media Anugerah} and Wong, {Kok Wai} and Hidayanto, {Achmad Nizar}",

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Rychalska, B, Bąbel, P, Gołuchowski, K, Michałowski, A, Dąbrowski, J & Biecek, P 2021, Cleora: A Simple, Strong and Scalable Graph Embedding Scheme. in T Mantoro, M Lee, MA Ayu, KW Wong & AN Hidayanto (eds), Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13111 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 338-352, 28th International Conference on Neural Information Processing, ICONIP 2021, Virtual, Online, 8/12/21. https://doi.org/10.1007/978-3-030-92273-3_28

Cleora: A Simple, Strong and Scalable Graph Embedding Scheme. / Rychalska, Barbara; Bąbel, Piotr; Gołuchowski, Konrad et al.
Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. ed. / Teddy Mantoro; Minho Lee; Media Anugerah Ayu; Kok Wai Wong; Achmad Nizar Hidayanto. Springer Science and Business Media Deutschland GmbH, 2021. p. 338-352 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13111 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Cleora

T2 - 28th International Conference on Neural Information Processing, ICONIP 2021

AU - Rychalska, Barbara

AU - Bąbel, Piotr

AU - Gołuchowski, Konrad

AU - Michałowski, Andrzej

AU - Dąbrowski, Jacek

AU - Biecek, Przemysław

PY - 2021

Y1 - 2021

N2 - The area of graph embeddings is currently dominated by contrastive learning methods, which demand formulation of an explicit objective function and sampling of positive and negative examples. One of the leading class of models are graph convolutional networks (GCNs), which suffer from numerous performance issues. In this paper we present Cleora: a purely unsupervised and highly scalable graph embedding scheme. Cleora can be likened to a GCN stripped down to its most effective core operation - the repeated neighborhood aggregation. Cleora does not require the application of a GPU and can embed massive graphs on CPU only, beating other state-of-the-art CPU algorithms in terms of speed and quality as measured on downstream tasks. Cleora has been applied in top machine learning competitions involving recommendations and graph processing, taking the podium in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We open-source Cleora under the MIT license allowing commercial use under https://github.com/Synerise/cleora.

AB - The area of graph embeddings is currently dominated by contrastive learning methods, which demand formulation of an explicit objective function and sampling of positive and negative examples. One of the leading class of models are graph convolutional networks (GCNs), which suffer from numerous performance issues. In this paper we present Cleora: a purely unsupervised and highly scalable graph embedding scheme. Cleora can be likened to a GCN stripped down to its most effective core operation - the repeated neighborhood aggregation. Cleora does not require the application of a GPU and can embed massive graphs on CPU only, beating other state-of-the-art CPU algorithms in terms of speed and quality as measured on downstream tasks. Cleora has been applied in top machine learning competitions involving recommendations and graph processing, taking the podium in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We open-source Cleora under the MIT license allowing commercial use under https://github.com/Synerise/cleora.

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KW - Graph embedding

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DO - 10.1007/978-3-030-92273-3_28

M3 - Conference contribution

AN - SCOPUS:85121909899

SN - 9783030922726

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 338

EP - 352

BT - Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings

A2 - Mantoro, Teddy

A2 - Lee, Minho

A2 - Ayu, Media Anugerah

A2 - Wong, Kok Wai

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PB - Springer Science and Business Media Deutschland GmbH

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Rychalska B, Bąbel P, Gołuchowski K, Michałowski A, Dąbrowski J, Biecek P. Cleora: A Simple, Strong and Scalable Graph Embedding Scheme. In Mantoro T, Lee M, Ayu MA, Wong KW, Hidayanto AN, editors, Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 338-352. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-92273-3_28