Link prediction in signed networks

Roshni Chakraborty, Ritwika Das, Nilotpal Chakraborty

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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.

TitelProceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020
Antal sider2
ForlagAssociation for Computing Machinery
Publikationsdato13 jul. 2020
ISBN (Elektronisk)978-1-4503-7098-1
StatusUdgivet - 13 jul. 2020
Begivenhed31st ACM Conference on Hypertext and Social Media, HT 2020 - Virtual, Online, USA
Varighed: 13 jul. 202015 jul. 2020


Konference31st ACM Conference on Hypertext and Social Media, HT 2020
ByVirtual, Online
SponsorACM Special Interest Group on Computer-Human Interaction (SIGCHI), ACM Special Interest Group on Hypertext, Hypermedia, and Web (SIGWEB)

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