5 citations · 5 across the 2 of their papers we have counts for
4 papers
Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath +2
Graph Neural Networks learn on graph-structured data by iteratively aggregating local neighborhood information. While this local message passing paradigm imparts a powerful inducti…
Quality Measures for Dynamic Graph Generative Models
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath +2
Deep generative models have recently achieved significant success in modeling graph data, including dynamic graphs, where topology and features evolve over time. However, unlike in…
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath +1
Recent advancements in graph representation learning have shifted attention towards dynamic graphs, which exhibit evolving topologies and features over time. The increased use of s…
Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
Ryien Hosseini, Filippo Simini, Austin Clyde +1
The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discov…