3 papers
cs.LG2026
Learning the Graph and the Embedding Together: Classifier-Independent Rewiring for Heterophilic Node Classification
Harshit Kumar, Sujan Chakraborty, Priyanka Saha +2
Graph neural networks lose much of their advantage on heterophilic graphs, where connected nodes often carry different labels. Graph rewiring is a popular remedy, but rewiring meth…
cs.LG2026
Fast and Featureless Node Representation Learning with Partial Pairwise Supervision
Sujan Chakraborty, Saptarshi Bej
We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node labels and no available node…
cs.LG2025
FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimization
Sujan Chakraborty, Rahul Bordoloi, Anindya Sengupta +2
Graph-based learning is a cornerstone for analyzing structured data, with node classification as a central task. However, in many real-world graphs, nodes lack informative feature…