Nederlands(NL) Deep graph kernel point processes over networks

新的点过程模型使用GNN进行网络事件预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新颖的点过程模型，用于处理网络上发生的离散事件数据。该模型集成了图神经网络（GNN）来表示影响核，增强了对历史事件对未来事件影响的捕捉能力。通过将统计方法与深度学习相结合，该方法旨在提高模型估计和预测精度，尤其适用于非欧几里得、图结构数据，优于现有的基于神经网络的强度函数模型。 AI

影响引入了一种对网络上的复杂事件数据进行建模的新颖方法，有望提高各种应用中的预测精度。

排序理由学术论文，详细介绍了新的模型架构及其实验验证。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 24 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了新的模型架构及其实验验证。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 Nederlands(NL) · Zheng Dong, Matthew Repasky, Xiuyuan Cheng, Yao Xie · 2026-09-01 04:00

网络上的深度图核点过程

arXiv:2306.11313v5 Announce Type: replace-cross Abstract: Point process models are widely used for continuous-time discrete-event data, where each data point includes time and additional information called "marks," such as locations, nodes, or event types. We present a new point …

本文如何被排名

报道来源 [1]

网络上的深度图核点过程

相关实体

相关话题