arXiv subjects

Gökhan Göktürk

Publications and source records attributed to Gökhan Göktürk.

3 recordsLinked to original sources

arXiv2026

Algorithmic Simplification for Million-Vertex Diffusion History Reconstruction

Diffusion history reconstruction infers latent node states between sparse observations of SI or SIR processes. HERMES combines parameter fitting, a learned graph-neural proposal, and feasibility-aware Markov chain Monte Carlo. We remove these stages one at a time and evaluate each version on all 12 canonical datasets. The final method uses deterministic mean-field forward-backward inference, threshold decoding, and fixed rates. This fixed-rate variant, Battus-Z, achieves mean macro-F1 of 0.8726 and NRMSE of 0.1010, compared with published HERMES aggregates of 0.8692 and 0.1483. The benchmark pins the final observed frame before scoring, so we also exclude all observed frames. Under this metric, Battus-Z obtains macro-F1 0.8431 and NRMSE 0.1181. Thus, the learned proposal, MCMC, and fitting stages can be removed while retaining the published aggregate quality on the evaluated HERMES benchmark and scoring protocol. A CUDA implementation processes generated histories with up to 4.84M vertices on LiveJournal and 117M edges on Orkut. On the same CUDA backend, Battus-Z reduces the geometric-mean algorithm interval relative to fitted Battus by 5.1x for SI and 20.3x for SIR. Its event-weighted causal-violation rates are 7.50% for SI and 8.77% for SIR; graph-constrained decoding remains future work.

cs.SI↗

arXiv2025

Approximating Spanning Centrality with Random Bouquets

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. Specifically, it quantifies how critical an edge is in terms of the number of spanning trees that include that edge. The current state-of-the-art for All Edges Spanning Centrality~(AESC), which computes the exact centrality values for all the edges, has a time complexity of $\mathcal{O}(mn^{3/2})$ for $n$ vertices and $m$ edges. This makes the computation infeasible even for moderately sized graphs. Instead, there exist approximation algorithms which process a large number of random walks to estimate edge centralities. However, even the approximation algorithms can be computationally overwhelming, especially if the approximation error bound is small. In this work, we propose a novel, hash-based sampling method and a vectorized algorithm which greatly improves the execution time by clustering random walks into {\it Bouquets}. On synthetic random walk benchmarks, {\it Bouquets} performs $7.8\times$ faster compared to naive, traditional random-walk generation. We also show that the proposed technique is scalable by employing it within a state-of-the-art AESC approximation algorithm, {\sc TGT+}. The experiments show that using Bouquets yields more than $100\times$ speed-up via parallelization with 16 threads.

cs.SI↗

arXiv2024

DiFuseR: A Distributed Sketch-based Influence Maximization Algorithm for GPUs

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 of finding an optimal seed set, approximation algorithms are often used for IM. However, these algorithms require a large number of simulations to find good seed sets. In this work, we propose DiFuseR, a blazing-fast, high-quality IM algorithm that can run on multiple GPUs in a distributed setting. DiFuseR is designed to increase GPU utilization, reduce inter-node communication, and minimize overlapping data/computation among the nodes. Based on the experiments with various graphs, containing some of the largest networks available, and diffusion settings, the proposed approach is found to be 3.2x and 12x faster on average on a single GPU and 8 GPUs, respectively. It can achieve up to 8x and 233.7x speedup on the same hardware settings. Furthermore, thanks to its smart load-balancing mechanism, on 8 GPUs, it is on average 5.6x faster compared to its single-GPU performance.

cs.DC↗