Link Prediction in Signed Networks
| العنوان: | Link Prediction in Signed Networks |
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| المؤلفون: | Ritwika Das, Nilotpal Chakraborty, Roshni Chakraborty |
| المصدر: | HT Chakraborty, R, Das, R & Chakraborty, N 2020, Link prediction in signed networks . in Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020 . Association for Computing Machinery, pp. 235-236, 31st ACM Conference on Hypertext and Social Media, HT 2020, Virtual, Online, United States, 13/07/2020 . https://doi.org/10.1145/3372923.3404805 |
| بيانات النشر: | ACM, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Signed network, business.industry, Computer science, Node (networking), Link prediction, Machine learning, computer.software_genre, Discriminative model, Embedding, Artificial intelligence, Macro, Generative adversarial network, business, Focus (optics), Link (knot theory), Structural balance theory, computer, Generative grammar |
| الوصف: | 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. |
| DOI: | 10.1145/3372923.3404805 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de195336fcf3a50b1c33fb648c70034e https://doi.org/10.1145/3372923.3404805 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....de195336fcf3a50b1c33fb648c70034e |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.1145/3372923.3404805 |
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