Jayadeva

most citedNo prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.SI2025

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening

Mohit Kataria, Shreyash Bhilwade, Sandeep Kumar +1

$Graph Coarsening (GC)$ is a prominent graph reduction technique that compresses large graphs to enable efficient learning and inference. However, existing GC methods gene…

cs.LG2025

GraphFLEx: Structure Learning Framework for Large Expanding Graphs

Mohit Kataria, Nikita Malik, Sandeep Kumar +1

Graph structure learning is a core problem in graph-based machine learning, essential for uncovering latent relationships and ensuring model interpretability. However, most existin…

cs.IR2023★ 2 cited

No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation

Nimesh Agrawal, Anuj Kumar Sirohi, Jayadeva +1

Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, fin…