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SNAP-tFDP: Massively Scalable Graph Layouts via Sparse Negative Sampling

Graphics 2026-08-03 v1

Abstract

Force-Directed Placement (FDP) is a widely used approach for network visualization, yet scaling it to massive graphs while preserving clear community structures remains a major computational and visual challenge. Existing approximation methods often rely on auxiliary data structures (e.g., spatial trees), which introduce substantial memory overhead; furthermore, traditional power-function-based forces frequently fail to separate dense clusters effectively. In this paper, we present a negative sampling-based algorithm that achieves O(|E|) time complexity with a low memory footprint, without requiring complex multi-level representations. In a first step, we introduce a linearly normalized degree-weighting scheme, which, combined with short-range bounded tt-distribution forces, effectively untangles dense structures and enhances visual cluster separation. To optimize for this formulation efficiently, we introduce an edge-centric negative sampling strategy that naturally reconstructs the global degree-weighted objective. Furthermore, we design a lock-free, bundle-based parallelization scheme that leverages the sparsity of stochastic updates to achieve significant speedups while mitigating access conflicts. Comprehensive evaluations on 12 large-scale graphs demonstrate that the proposed method outperforms state-of-the-art algorithms in neighborhood preservation and cluster separation. Compared to existing baselines, our method reduces memory consumption by 72% on average and leverages simple GPU parallelism to generate a high-quality layout for a graph with 4 million nodes and 34 million edges in below 10 seconds.

Cite

@article{arxiv.2608.01907,
  title  = {SNAP-tFDP: Massively Scalable Graph Layouts via Sparse Negative Sampling},
  author = {Xin Chen and Shuowei Hou and Yifan Wang and Mingliang Xue and Zezheng Feng and Oliver Deussen and Weidong Huang and Yunhai Wang},
  journal= {arXiv preprint arXiv:2608.01907},
  year   = {2026}
}

Comments

Accepted by IEEE VIS 2026