99 citations · 118 across the 6 of their papers we have counts for
6 papers
Geo: A Query Rewrite Framework for Graph Pattern Mining
Nazanin Yousefian, Kasra Jamshidi, Keval Vora +1
Graph pattern mining is important for analyzing graph data. Graph mining systems typically require answering pattern matching queries, which involve solving the NP-complete subgrap…
Estimating Power-Law Exponent with Edge Differential Privacy
Adam Tan, Mohamed Hefny, Keval Vora
Many real-world graphs have degree distributions that are well approximated by a power-law, and the corresponding scaling parameter provides a compact summary of that structure…
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…