3 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
Unifying Graph Neural Networks Through a Common Layer Equation
Sai Karthik Navuluru, Siddhartha Shankar Das, Bo Ni +9
Graph neural networks are commonly described through family-specific equations whose notation obscures shared computations and structural differences. We introduce a common layer e…
SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks
Siddhartha Shankar Das, Naheed Anjum Arafat, Muftiqur Rahman +3
We propose SGS-GNN, a novel supervised graph sparsifier that learns the sampling probability distribution of edges and samples sparse subgraphs of a user-specified size to reduce t…
AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
Siddhartha Shankar Das, S M Ferdous, Mahantesh M Halappanavar +2
We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneou…
V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities
Siddhartha Shankar Das, Edoardo Serra, Mahantesh Halappanavar +2
Weaknesses in computer systems such as faults, bugs and errors in the architecture, design or implementation of software provide vulnerabilities that can be exploited by attackers…