SubRank: Subgraph Embeddings via a Subgraph Proximity Measure

Oana Balalau; Sagar Goyal

doi:10.1007/978-3-030-47426-3_38

SubRank: Subgraph Embeddings via a Subgraph Proximity Measure

Oana Balalau
, Sagar Goyal

Microsoft Corporation

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

Abstract

Representation learning for graph data has gained a lot of attention in recent years. However, state-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings. Subgraph embeddings have many applications, such as community detection, cascade prediction, and question answering. In this work, we propose a subgraph to subgraph proximity measure as a building block for a subgraph embedding framework. Experiments on real-world datasets show that our approach, SubRank, outperforms state-of-the-art methods on several important data mining tasks.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
Editors	Hady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
Publisher	Springer
Pages	487-498
Number of pages	12
ISBN (Print)	9783030474256
DOIs	https://doi.org/10.1007/978-3-030-47426-3_38
Publication status	Published - 1 Jan 2020
Event	24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore Duration: 11 May 2020 → 14 May 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12084 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
Country/Territory	Singapore
City	Singapore
Period	11/05/20 → 14/05/20

Keywords

Personalized PageRank
Subgraph embeddings

Access to Document

10.1007/978-3-030-47426-3_38

Cite this

Balalau, O., & Goyal, S. (2020). SubRank: Subgraph Embeddings via a Subgraph Proximity Measure. In H. W. Lauw, E.-P. Lim, R. C.-W. Wong, A. Ntoulas, S.-K. Ng, & S. J. Pan (Eds.), Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings (pp. 487-498). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12084 LNAI). Springer. https://doi.org/10.1007/978-3-030-47426-3_38

Balalau, Oana ; Goyal, Sagar. / SubRank : Subgraph Embeddings via a Subgraph Proximity Measure. Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. editor / Hady W. Lauw ; Ee-Peng Lim ; Raymond Chi-Wing Wong ; Alexandros Ntoulas ; See-Kiong Ng ; Sinno Jialin Pan. Springer, 2020. pp. 487-498 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Representation learning for graph data has gained a lot of attention in recent years. However, state-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings. Subgraph embeddings have many applications, such as community detection, cascade prediction, and question answering. In this work, we propose a subgraph to subgraph proximity measure as a building block for a subgraph embedding framework. Experiments on real-world datasets show that our approach, SubRank, outperforms state-of-the-art methods on several important data mining tasks.",

keywords = "Personalized PageRank, Subgraph embeddings",

author = "Oana Balalau and Sagar Goyal",

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year = "2020",

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Balalau, O & Goyal, S 2020, SubRank: Subgraph Embeddings via a Subgraph Proximity Measure. in HW Lauw, E-P Lim, RC-W Wong, A Ntoulas, S-K Ng & SJ Pan (eds), Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12084 LNAI, Springer, pp. 487-498, 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, Singapore, Singapore, 11/05/20. https://doi.org/10.1007/978-3-030-47426-3_38

SubRank: Subgraph Embeddings via a Subgraph Proximity Measure. / Balalau, Oana; Goyal, Sagar.
Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. ed. / Hady W. Lauw; Ee-Peng Lim; Raymond Chi-Wing Wong; Alexandros Ntoulas; See-Kiong Ng; Sinno Jialin Pan. Springer, 2020. p. 487-498 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12084 LNAI).

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

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T2 - 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020

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AU - Goyal, Sagar

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

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Y1 - 2020/1/1

N2 - Representation learning for graph data has gained a lot of attention in recent years. However, state-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings. Subgraph embeddings have many applications, such as community detection, cascade prediction, and question answering. In this work, we propose a subgraph to subgraph proximity measure as a building block for a subgraph embedding framework. Experiments on real-world datasets show that our approach, SubRank, outperforms state-of-the-art methods on several important data mining tasks.

AB - Representation learning for graph data has gained a lot of attention in recent years. However, state-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings. Subgraph embeddings have many applications, such as community detection, cascade prediction, and question answering. In this work, we propose a subgraph to subgraph proximity measure as a building block for a subgraph embedding framework. Experiments on real-world datasets show that our approach, SubRank, outperforms state-of-the-art methods on several important data mining tasks.

KW - Personalized PageRank

KW - Subgraph embeddings

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DO - 10.1007/978-3-030-47426-3_38

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SN - 9783030474256

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

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EP - 498

BT - Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings

A2 - Lauw, Hady W.

A2 - Lim, Ee-Peng

A2 - Wong, Raymond Chi-Wing

A2 - Ntoulas, Alexandros

A2 - Ng, See-Kiong

A2 - Pan, Sinno Jialin

PB - Springer

Y2 - 11 May 2020 through 14 May 2020

ER -

Balalau O, Goyal S. SubRank: Subgraph Embeddings via a Subgraph Proximity Measure. In Lauw HW, Lim EP, Wong RCW, Ntoulas A, Ng SK, Pan SJ, editors, Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. Springer. 2020. p. 487-498. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-47426-3_38

SubRank: Subgraph Embeddings via a Subgraph Proximity Measure

Abstract

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Keywords

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