Link prediction in signed networks

Roshni Chakraborty; Ritwika Das; Nilotpal Chakraborty

doi:10.1145/3372923.3404805

Link prediction in signed networks

Roshni Chakraborty, Ritwika Das, Nilotpal Chakraborty

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

1 Citationer (Scopus)

Abstract

Signed networks represent the real world relationships, which are both positive or negative. Recent research works focus on either discriminative or generative based models for signed network embedding. In this paper, we propose a generative adversarial network (GAN) model for signed network which unifies generative and discriminative models to generate the node embedding. Our experimental evaluations on several datasets, like Slashdot, Epinions, Reddit, Bitcoin and Wiki-RFA indicates that the proposed approach ensures better macro F1-score than the existing state-of-the-art approaches in link prediction and handling of sparsity of signed networks.

Originalsprog	Engelsk
Titel	Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020
Antal sider	2
Forlag	Association for Computing Machinery
Publikationsdato	13 jul. 2020
Sider	235-236
ISBN (Elektronisk)	978-1-4503-7098-1
DOI	https://doi.org/10.1145/3372923.3404805
Status	Udgivet - 13 jul. 2020
Begivenhed	31st ACM Conference on Hypertext and Social Media, HT 2020 - Virtual, Online, USA Varighed: 13 jul. 2020 → 15 jul. 2020

Konference

Konference	31st ACM Conference on Hypertext and Social Media, HT 2020
Land/Område	USA
By	Virtual, Online
Periode	13/07/2020 → 15/07/2020
Sponsor	ACM Special Interest Group on Computer-Human Interaction (SIGCHI), ACM Special Interest Group on Hypertext, Hypermedia, and Web (SIGWEB)

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Publisher Copyright:
© 2020 Owner/Author.

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10.1145/3372923.3404805

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Citationsformater

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title = "Link prediction in signed networks",

abstract = "Signed networks represent the real world relationships, which are both positive or negative. Recent research works focus on either discriminative or generative based models for signed network embedding. In this paper, we propose a generative adversarial network (GAN) model for signed network which unifies generative and discriminative models to generate the node embedding. Our experimental evaluations on several datasets, like Slashdot, Epinions, Reddit, Bitcoin and Wiki-RFA indicates that the proposed approach ensures better macro F1-score than the existing state-of-the-art approaches in link prediction and handling of sparsity of signed networks.",

keywords = "Generative adversarial network, Link prediction, Signed network, Structural balance theory",

author = "Roshni Chakraborty and Ritwika Das and Nilotpal Chakraborty",

note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 31st ACM Conference on Hypertext and Social Media, HT 2020 ; Conference date: 13-07-2020 Through 15-07-2020",

year = "2020",

month = jul,

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doi = "10.1145/3372923.3404805",

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AU - Chakraborty, Roshni

AU - Das, Ritwika

AU - Chakraborty, Nilotpal

PY - 2020/7/13

Y1 - 2020/7/13

N2 - Signed networks represent the real world relationships, which are both positive or negative. Recent research works focus on either discriminative or generative based models for signed network embedding. In this paper, we propose a generative adversarial network (GAN) model for signed network which unifies generative and discriminative models to generate the node embedding. Our experimental evaluations on several datasets, like Slashdot, Epinions, Reddit, Bitcoin and Wiki-RFA indicates that the proposed approach ensures better macro F1-score than the existing state-of-the-art approaches in link prediction and handling of sparsity of signed networks.

AB - Signed networks represent the real world relationships, which are both positive or negative. Recent research works focus on either discriminative or generative based models for signed network embedding. In this paper, we propose a generative adversarial network (GAN) model for signed network which unifies generative and discriminative models to generate the node embedding. Our experimental evaluations on several datasets, like Slashdot, Epinions, Reddit, Bitcoin and Wiki-RFA indicates that the proposed approach ensures better macro F1-score than the existing state-of-the-art approaches in link prediction and handling of sparsity of signed networks.

KW - Generative adversarial network

KW - Link prediction

KW - Signed network

KW - Structural balance theory

UR - http://www.scopus.com/inward/record.url?scp=85089503602&partnerID=8YFLogxK

U2 - 10.1145/3372923.3404805

DO - 10.1145/3372923.3404805

M3 - Article in proceeding

AN - SCOPUS:85089503602

SP - 235

EP - 236

BT - Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020

PB - Association for Computing Machinery

T2 - 31st ACM Conference on Hypertext and Social Media, HT 2020

Y2 - 13 July 2020 through 15 July 2020

ER -

Link prediction in signed networks

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