Fault Lines of Refugee Exclusion: Statelessness, Gender, and COVID-19 in South Asia

Autor: Roshni Chakraborty, Jacqueline Bhabha

Publikováno v: Health and Human Rights, Vol 23, Iss 1, Pp 237-250 (2020)

Despite widespread recognition of the right to a nationality, statelessness and its attendant vulnerabilities continue to characterize the lives of millions in South Asia. During the onset of the COVID-19 pandemic, when states turned inward to protec

Externí odkaz: https://doaj.org/article/c6b6cd21ba934116b3a0b7e19d60faad

Finding Representative Sampling Subsets in Sensor Graphs using Time-Series Similarities

Autor: Roshni Chakraborty, Josefine Holm, Torben Bach Pedersen, Petar Popovski

Publikováno v: ACM Transactions on Sensor Networks.

With the increasing use of IoT-enabled sensors, it is important to have effective methods to query the sensors. For example, in a dense network of battery-driven temperature sensors, it is often possible to query (sample) only a subset of the sensors

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::244309c6c94f3d120998661c69904ac2
https://doi.org/10.1145/3595181

SigGAN : Adversarial Model for Learning Signed Relationships in Networks

Autor: Roshni Chakraborty, Ritwika Das, Joydeep Chandra

Signed link prediction in graphs is an important problem that has applications in diverse domains. It is a binary classification problem that predicts whether an edge between a pair of nodes is positive or negative. Existing approaches for link predi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::186d46fe7ae97f45cf6fd01c2c696ae5

HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment

Autor: Shruti Saxena, Roshni Chakraborty, Joydeep Chandra

Publikováno v: Information Processing & Management. 59:103021

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e3c251d0b5dbab665413fc4d6f039294
https://doi.org/10.1016/j.ipm.2022.103021

Link Prediction in Signed Networks

Autor: Ritwika Das, Nilotpal Chakraborty, Roshni Chakraborty

Publikováno v: 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

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 adversar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de195336fcf3a50b1c33fb648c70034e
https://doi.org/10.1145/3372923.3404805