99 citations · 118 across the 4 of their papers we have counts for
4 papers
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
John Thorpe, Yifan Qiao, Jonathan Eyolfson +8
A graph neural network (GNN) enables deep learning on structured graph data. There are two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which are e…
Pattern Morphing for Efficient Graph Mining
Kasra Jamshidi, Keval Vora
Graph mining applications analyze the structural properties of large graphs, and they do so by finding subgraph isomorphisms, which makes them computationally intensive. Existing g…
GraFS: Graph Analytics Fusion and Synthesis
Farzin Houshmand, Mohsen Lesani, Keval Vora
Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient…
Peregrine: A Pattern-Aware Graph Mining System
Kasra Jamshidi, Rakesh Mahadasa, Keval Vora
Graph mining workloads aim to extract structural properties of a graph by exploring its subgraph structures. General purpose graph mining systems provide a generic runtime to explo…