Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification

Md Tanvir Alam; Chowdhury Farhan Ahmed; Md Samiullah; Carson K. Leung

doi:10.1007/978-3-030-75765-6_2

Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification

Md Tanvir Alam
, Chowdhury Farhan Ahmed
, Md Samiullah
, Carson K. Leung

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

28 Citations (Scopus)

Abstract

Graph is used to represent various complex relationships among objects and data entities. One of the emerging and important problems is graph classification that has tremendous impacts on various real-life applications. A good number of approaches have been proposed for graph classification using various techniques where graph embedding is one of them. Here we propose an approach for classifying graphs by mining discriminating frequent patterns from graphs to learn vector representation of the graphs. The proposed supervised embedding technique produces high-quality entire graph embedding for classification utilizing the knowledge from the labeled examples available. The experimental analyses, conducted on various real-life benchmark datasets, found that the proposed approach is significantly better in terms of accuracy in comparison to the state-of-the-art techniques.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings
Editors	Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	16-28
Number of pages	13
ISBN (Electronic)	9783030757656
ISBN (Print)	9783030757649
DOIs	https://doi.org/10.1007/978-3-030-75765-6_2
Publication status	Published - 2021
Externally published	Yes
Event	Pacific-Asia Conference on Knowledge Discovery and Data Mining 2021 - Virtual, Delhi, India Duration: 11 May 2021 → 14 May 2021 Conference number: 25th https://www.pakdd2021.org (Website) https://link.springer.com/book/10.1007/978-3-030-75765-6 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	12713
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Pacific-Asia Conference on Knowledge Discovery and Data Mining 2021
Abbreviated title	PAKDD 2021
Country/Territory	India
City	Delhi
Period	11/05/21 → 14/05/21
Internet address	https://www.pakdd2021.org (Website) https://link.springer.com/book/10.1007/978-3-030-75765-6 (Proceedings)

Keywords

Discriminating pattern mining
Frequent pattern mining
Graph classification
Graph mining
Pattern mining

Access to Document

10.1007/978-3-030-75765-6_2

Cite this

Alam, M. T., Ahmed, C. F., Samiullah, M., & Leung, C. K. (2021). Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification. In K. Karlapalem, H. Cheng, N. Ramakrishnan, R. K. Agrawal, P. K. Reddy, J. Srivastava, & T. Chakraborty (Eds.), Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings (pp. 16-28). (Lecture Notes in Computer Science; Vol. 12713). Springer. https://doi.org/10.1007/978-3-030-75765-6_2

Alam, Md Tanvir ; Ahmed, Chowdhury Farhan ; Samiullah, Md et al. / Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification. Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings. editor / Kamal Karlapalem ; Hong Cheng ; Naren Ramakrishnan ; R. K. Agrawal ; P. Krishna Reddy ; Jaideep Srivastava ; Tanmoy Chakraborty. Cham Switzerland : Springer, 2021. pp. 16-28 (Lecture Notes in Computer Science).

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title = "Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification",

abstract = "Graph is used to represent various complex relationships among objects and data entities. One of the emerging and important problems is graph classification that has tremendous impacts on various real-life applications. A good number of approaches have been proposed for graph classification using various techniques where graph embedding is one of them. Here we propose an approach for classifying graphs by mining discriminating frequent patterns from graphs to learn vector representation of the graphs. The proposed supervised embedding technique produces high-quality entire graph embedding for classification utilizing the knowledge from the labeled examples available. The experimental analyses, conducted on various real-life benchmark datasets, found that the proposed approach is significantly better in terms of accuracy in comparison to the state-of-the-art techniques.",

keywords = "Discriminating pattern mining, Frequent pattern mining, Graph classification, Graph mining, Pattern mining",

author = "Alam, \{Md Tanvir\} and Ahmed, \{Chowdhury Farhan\} and Md Samiullah and Leung, \{Carson K.\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; Pacific-Asia Conference on Knowledge Discovery and Data Mining 2021, PAKDD 2021 ; Conference date: 11-05-2021 Through 14-05-2021",

year = "2021",

doi = "10.1007/978-3-030-75765-6\_2",

language = "English",

isbn = "9783030757649",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "16--28",

editor = "Kamal Karlapalem and Hong Cheng and Naren Ramakrishnan and Agrawal, \{R. K.\} and Reddy, \{P. Krishna\} and Jaideep Srivastava and Tanmoy Chakraborty",

booktitle = "Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings",

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Alam, MT, Ahmed, CF, Samiullah, M & Leung, CK 2021, Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification. in K Karlapalem, H Cheng, N Ramakrishnan, RK Agrawal, PK Reddy, J Srivastava & T Chakraborty (eds), Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings. Lecture Notes in Computer Science, vol. 12713, Springer, Cham Switzerland, pp. 16-28, Pacific-Asia Conference on Knowledge Discovery and Data Mining 2021, Delhi, India, 11/05/21. https://doi.org/10.1007/978-3-030-75765-6_2

Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification. / Alam, Md Tanvir; Ahmed, Chowdhury Farhan; Samiullah, Md et al.
Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings. ed. / Kamal Karlapalem; Hong Cheng; Naren Ramakrishnan; R. K. Agrawal; P. Krishna Reddy; Jaideep Srivastava; Tanmoy Chakraborty. Cham Switzerland: Springer, 2021. p. 16-28 (Lecture Notes in Computer Science; Vol. 12713).

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

TY - GEN

T1 - Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification

AU - Alam, Md Tanvir

AU - Ahmed, Chowdhury Farhan

AU - Samiullah, Md

AU - Leung, Carson K.

N1 - Conference code: 25th

PY - 2021

Y1 - 2021

N2 - Graph is used to represent various complex relationships among objects and data entities. One of the emerging and important problems is graph classification that has tremendous impacts on various real-life applications. A good number of approaches have been proposed for graph classification using various techniques where graph embedding is one of them. Here we propose an approach for classifying graphs by mining discriminating frequent patterns from graphs to learn vector representation of the graphs. The proposed supervised embedding technique produces high-quality entire graph embedding for classification utilizing the knowledge from the labeled examples available. The experimental analyses, conducted on various real-life benchmark datasets, found that the proposed approach is significantly better in terms of accuracy in comparison to the state-of-the-art techniques.

AB - Graph is used to represent various complex relationships among objects and data entities. One of the emerging and important problems is graph classification that has tremendous impacts on various real-life applications. A good number of approaches have been proposed for graph classification using various techniques where graph embedding is one of them. Here we propose an approach for classifying graphs by mining discriminating frequent patterns from graphs to learn vector representation of the graphs. The proposed supervised embedding technique produces high-quality entire graph embedding for classification utilizing the knowledge from the labeled examples available. The experimental analyses, conducted on various real-life benchmark datasets, found that the proposed approach is significantly better in terms of accuracy in comparison to the state-of-the-art techniques.

KW - Discriminating pattern mining

KW - Frequent pattern mining

KW - Graph classification

KW - Graph mining

KW - Pattern mining

UR - https://www.scopus.com/pages/publications/85111115783

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DO - 10.1007/978-3-030-75765-6_2

M3 - Conference Paper

AN - SCOPUS:85111115783

SN - 9783030757649

T3 - Lecture Notes in Computer Science

SP - 16

EP - 28

BT - Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings

A2 - Karlapalem, Kamal

A2 - Cheng, Hong

A2 - Ramakrishnan, Naren

A2 - Agrawal, R. K.

A2 - Reddy, P. Krishna

A2 - Srivastava, Jaideep

A2 - Chakraborty, Tanmoy

PB - Springer

CY - Cham Switzerland

T2 - Pacific-Asia Conference on Knowledge Discovery and Data Mining 2021

Y2 - 11 May 2021 through 14 May 2021

ER -

Alam MT, Ahmed CF, Samiullah M, Leung CK. Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification. In Karlapalem K, Cheng H, Ramakrishnan N, Agrawal RK, Reddy PK, Srivastava J, Chakraborty T, editors, Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings. Cham Switzerland: Springer. 2021. p. 16-28. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-75765-6_2

Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification

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Keywords

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