AI 中文总结
针对大规模图力导向布局的挑战，SNAP-tFDP算法结合度加权t分布力、边中心负采样与无锁并行，在12个大图上实现更优簇分离，内存降72%，400万节点图布局耗时不足10秒。
AI 中文摘要
力导向布局（Force-Directed Placement, FDP）是网络可视化中广泛应用的方法，但将其扩展到大规模图并保留清晰的社区结构仍是计算与视觉层面的重大挑战。现有近似方法常依赖空间树等辅助数据结构，带来大量内存开销；此外，传统基于幂函数的力常无法有效分离密集簇。本文提出一种基于负采样的算法，实现O(|E|)时间复杂度且内存占用低，无需复杂多级表示。第一步引入线性归一化度加权方案，结合短程有界t分布力，可有效解开密集结构并增强视觉簇分离；为高效优化该公式，提出以边为中心的负采样策略，自然重构全局度加权目标；还设计了无锁、基于束的并行方案，利用随机更新的稀疏性实现显著加速，同时缓解访问冲突。对12个大规模图的综合评估表明，该方法在邻域保留和簇分离上优于现有最先进算法；与现有基线相比，平均内存消耗降低72%，且利用简单GPU并行性，可在10秒内生成含400万节点、3400万边的图的高质量布局。
英文摘要
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 $t$-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.
CommentsAccepted by IEEE VIS 2026
