4 papers
Algorithmic Simplification for Million-Vertex Diffusion History Reconstruction
Gökhan Göktürk
Diffusion history reconstruction infers latent node states between sparse observations of SI or SIR processes. HERMES combines parameter fitting, a learned graph-neural proposal, a…
DiFuseR: A Distributed Sketch-based Influence Maximization Algorithm for GPUs
Gökhan Göktürk, Kamer Kaya
Influence Maximization (IM) aims to find a given number of "seed" vertices that can effectively maximize the expected spread under a given diffusion model. Due to the NP-Hardness o…
Approximating Spanning Centrality with Random Bouquets
Gökhan Göktürk, Kamer Kaya
Spanning Centrality is a measure used in network analysis to determine the importance of an edge in a graph based on its contribution to the connectivity of the entire network. Spe…
Fast and Error-Adaptive Influence Maximization based on Count-Distinct Sketches
Gokhan Gokturk, Kamer Kaya
Influence maximization (IM) is the problem of finding a seed vertex set that maximizes the expected number of vertices influenced under a given diffusion model. Due to the NP-Hardn…