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…
Accelerating Spanning-Centrality Approximation with Sampling-Aware Vectorized Random Walks
Gökhan Göktürk, Kamer Kaya
Spanning centrality quantifies an edge's importance through its share of a graph's spanning trees, and approximating it for all edges (AESC) requires processing large volumes of ir…
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…