Joint neighborhood subgraphs link prediction

Dinh Tran-Van; Alessandro Sperduti; Fabrizio Costa

doi:10.1007/978-3-319-70087-8_13

Joint neighborhood subgraphs link prediction

Dinh Tran-Van, Alessandro Sperduti, Fabrizio Costa^*

^*Corresponding author for this work

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

Abstract

A crucial computational task for relational and network data is the “link prediction problem” which allows for example to discover unknown interactions between proteins to explain the mechanism of a disease in biological networks, or to suggest novel products for a customer in a e-commerce recommendation system. Most link prediction approaches however do not effectively exploit the contextual information available in the neighborhood of each edge. Here we propose to cast the problem as a binary classification task over the union of the pair of subgraphs located at the endpoints of each edge. We model the classification task using a support vector machine endowed with an efficient graph kernel and achieve state-of-the-art results on several benchmark datasets.

Original language	English
Title of host publication	Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
Editors	Yuanqing Li, Derong Liu, Shengli Xie, El-Sayed M. El-Alfy, Dongbin Zhao
Publisher	Springer Verlag
Pages	117-123
Number of pages	7
ISBN (Print)	9783319700861
DOIs	https://doi.org/10.1007/978-3-319-70087-8_13
State	Published - 2017
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10634 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Graph kernels
Link prediction

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-70087-8_13

Cite this

Tran-Van, D., Sperduti, A., & Costa, F. (2017). Joint neighborhood subgraphs link prediction. In Y. Li, D. Liu, S. Xie, E.-S. M. El-Alfy, & D. Zhao (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (pp. 117-123). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10634 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70087-8_13

Tran-Van, Dinh ; Sperduti, Alessandro ; Costa, Fabrizio. / Joint neighborhood subgraphs link prediction. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. editor / Yuanqing Li ; Derong Liu ; Shengli Xie ; El-Sayed M. El-Alfy ; Dongbin Zhao. Springer Verlag, 2017. pp. 117-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A crucial computational task for relational and network data is the “link prediction problem” which allows for example to discover unknown interactions between proteins to explain the mechanism of a disease in biological networks, or to suggest novel products for a customer in a e-commerce recommendation system. Most link prediction approaches however do not effectively exploit the contextual information available in the neighborhood of each edge. Here we propose to cast the problem as a binary classification task over the union of the pair of subgraphs located at the endpoints of each edge. We model the classification task using a support vector machine endowed with an efficient graph kernel and achieve state-of-the-art results on several benchmark datasets.",

keywords = "Graph kernels, Link prediction",

author = "Dinh Tran-Van and Alessandro Sperduti and Fabrizio Costa",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.",

year = "2017",

doi = "10.1007/978-3-319-70087-8\_13",

language = "English",

isbn = "9783319700861",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "117--123",

editor = "Yuanqing Li and Derong Liu and Shengli Xie and El-Alfy, \{El-Sayed M.\} and Dongbin Zhao",

booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",

address = "Germany",

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Tran-Van, D, Sperduti, A & Costa, F 2017, Joint neighborhood subgraphs link prediction. in Y Li, D Liu, S Xie, E-SM El-Alfy & D Zhao (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10634 LNCS, Springer Verlag, pp. 117-123. https://doi.org/10.1007/978-3-319-70087-8_13

Joint neighborhood subgraphs link prediction. / Tran-Van, Dinh; Sperduti, Alessandro; Costa, Fabrizio.
Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. ed. / Yuanqing Li; Derong Liu; Shengli Xie; El-Sayed M. El-Alfy; Dongbin Zhao. Springer Verlag, 2017. p. 117-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10634 LNCS).

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

TY - GEN

T1 - Joint neighborhood subgraphs link prediction

AU - Tran-Van, Dinh

AU - Sperduti, Alessandro

AU - Costa, Fabrizio

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - A crucial computational task for relational and network data is the “link prediction problem” which allows for example to discover unknown interactions between proteins to explain the mechanism of a disease in biological networks, or to suggest novel products for a customer in a e-commerce recommendation system. Most link prediction approaches however do not effectively exploit the contextual information available in the neighborhood of each edge. Here we propose to cast the problem as a binary classification task over the union of the pair of subgraphs located at the endpoints of each edge. We model the classification task using a support vector machine endowed with an efficient graph kernel and achieve state-of-the-art results on several benchmark datasets.

AB - A crucial computational task for relational and network data is the “link prediction problem” which allows for example to discover unknown interactions between proteins to explain the mechanism of a disease in biological networks, or to suggest novel products for a customer in a e-commerce recommendation system. Most link prediction approaches however do not effectively exploit the contextual information available in the neighborhood of each edge. Here we propose to cast the problem as a binary classification task over the union of the pair of subgraphs located at the endpoints of each edge. We model the classification task using a support vector machine endowed with an efficient graph kernel and achieve state-of-the-art results on several benchmark datasets.

KW - Graph kernels

KW - Link prediction

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DO - 10.1007/978-3-319-70087-8_13

M3 - Conference contribution

AN - SCOPUS:85035135377

SN - 9783319700861

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

SP - 117

EP - 123

BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings

A2 - Li, Yuanqing

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A2 - Xie, Shengli

A2 - El-Alfy, El-Sayed M.

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PB - Springer Verlag

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Tran-Van D, Sperduti A, Costa F. Joint neighborhood subgraphs link prediction. In Li Y, Liu D, Xie S, El-Alfy ESM, Zhao D, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Springer Verlag. 2017. p. 117-123. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-70087-8_13

Joint neighborhood subgraphs link prediction

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this