3 citations · 5 across the 6 of their papers we have counts for
6 papers
ELRUHNA: Elimination Rule-basedHypergraph Alignment
Cameron Ibrahim, S M Ferdous, Ilya Safro +2
Hypergraph alignment is a well-known NP-hard problem with numerous practical applications across domains such as bioinformatics, social network analysis, and computer vision. Despi…
An Integrated Epidemic Simulation Workflow for Submodular Intervention Strategies
Reet Barik, Marco Minutoli, Mahantesh Halappanavar +1
Owing to the ongoing COVID-19 pandemic and other recent global epidemics, epidemic simulation frameworks are gaining rapid significance. In this work, we present a workflow that wi…
Learning to Simulate Aerosol Dynamics with Graph Neural Networks
Fabiana Ferracina, Payton Beeler, Mahantesh Halappanavar +3
Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models…
GreediRIS: Scalable Influence Maximization using Distributed Streaming Maximum Cover
Reet Barik, Wade Cappa, S M Ferdous +3
Influence maximization--the problem of identifying a subset of k influential seeds (vertices) in a network--is a classical problem in network science with numerous applications. Th…
Fused Breadth-First Probabilistic Traversals on Distributed GPU Systems
Reece Neff, Mostafa Eghbali Zarch, Marco Minutoli +4
Probabilistic breadth-first traversals (BPTs) are used in many network science and graph machine learning applications. In this paper, we are motivated by the application of BPTs i…
HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures
Xinyu Chen, Marco Minutoli, Jiannan Tian +3
Influence maximization aims to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. Although computing optimal seed se…