AI 中文总结
该研究简化了用于百万顶点扩散历史重建的HERMES算法，移除学习提议、MCMC和拟合阶段，提出的Battus-Z方法在保持性能的同时大幅提升效率，适用于大规模图数据。
AI 中文摘要
扩散历史重建用于推断SI或SIR过程稀疏观测之间的潜在节点状态。HERMES算法结合了参数拟合、学习到的图神经提议以及感知可行性的马尔可夫链蒙特卡洛（MCMC）。我们逐一移除这些阶段，并在全部12个基准数据集上评估每个版本。最终方法采用确定性平均场前向-后向推断、阈值解码和固定速率。该固定速率变体Battus-Z在HERMES基准测试及评分协议下，平均宏F1值达0.8726，归一化均方根误差（NRMSE）为0.1010，而已发表的HERMES综合结果分别为0.8692和0.1483。由于基准评分前固定最终观测帧，我们还排除所有观测帧，在此指标下，Battus-Z的宏F1为0.8431，NRMSE为0.1181。由此可见，可移除学习提议、MCMC和拟合阶段，同时在评估的HERMES基准及评分协议上保留已发表的综合性能。CUDA实现可处理LiveJournal上多达484万顶点、Orkut上1.17亿边的生成历史；在同一CUDA后端，Battus-Z相比拟合版Battus，几何平均算法间隔在SI任务上缩短5.1倍，SIR任务上缩短20.3倍，其事件加权因果违反率在SI任务为7.50%，SIR任务为8.77%，图约束解码为未来工作。
英文摘要
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.
Comments8 pages, 2 figures, 8 tables. Preliminary results and text
