5 citations · 6 across the 15 of their papers we have counts for
23 papers
Scalable extraction and visualization of multi-attribute logical and functional dependencies in tabular data
Chaithra Umesh, Arvind Lomrore, Neethu D +3
Understanding the structural relationships among attributes in tabular data is fundamental to machine learning and pattern recognition. While functional dependency (FD) discovery h…
HyperFuse: Fast Self-Supervised Node Embeddings for Attributed Hypergraphs
Megha P, Harshit Kumar, Srajan Agarwal +3
Self-supervised hypergraph representation learning can produce informative node embeddings, but existing methods often require deep encoders trained for hundreds of epochs, making…
Extremely Fast and Compact Binary Graph Representations via Randomized Operator Sketching
Srajan Agarwal, Megha P, Bikas C Das +2
Graph neural networks typically rely on dense, floating-point node representations, which can impose substantial memory and computational costs. Binary graph hashing offers an alte…
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…
HERALD: High-Fidelity Exemplar Retrieval with Adaptive Landmark Distillation for Heterophily-Aware Graph Condensation
Sujan Chakraborty, Priyanka Saha, Saptarshi Bej
Graph condensation aims to produce a small surrogate graph that preserves the downstream node-classification performance of a much larger original graph. Existing methods rely on W…
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…