English(EN) MambaTG: Candidate-Aware State Space Modeling for Future Link Prediction on Temporal Graphs

MambaTG框架增强时序图链接预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员推出MambaTG，一个用于时序图未来链接预测的新型框架。该模型通过有效处理重复和未见链接的预测，解决了现有方法的局限性。MambaTG采用候选感知方法，利用Mamba的选择性递归来模拟历史序列演变和输入依赖的状态转换，以实现候选-历史兼容性。在13个数据集上的实验表明，MambaTG表现出强大且一致的性能，在保持计算效率的同时，超越了13个基线模型。 AI

影响引入了一种新的时序图分析方法，有望改善动态网络场景下的预测。

排序理由详细介绍时序图分析新模型架构的学术论文。[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: 13 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍时序图分析新模型架构的学术论文。[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 English(EN) · Nguyen Hong Quan, Sitao Luan, Yilun Zheng, Lihui Chen · 2026-11-06 05:00

MambaTG：面向时序图未来链接预测的候选感知状态空间建模

arXiv:2611.07569v1 Announce Type: new Abstract: Future link prediction on temporal graphs involves both repeated interactions between previously connected node pairs and first-time interactions between pairs with no prior interaction. While repeated-edge prediction can exploit di…

本文如何被排名

报道来源 [1]

MambaTG：面向时序图未来链接预测的候选感知状态空间建模

相关实体

相关话题