This study delves into the application of graph neural networks in the realm
of traffic forecasting, a crucial facet of intelligent transportation systems.
Accurate traffic predictions are vital for functions like trip planning,
traffic control, and vehicle routing in such systems. Three prominent GNN
architectures Graph Convolutional Networks (Graph Sample and Aggregation) and
Gated Graph Neural Networks are explored within the context of traffic
prediction. Each architecture's methodology is thoroughly examined, including
layer configurations, activation functions,and hyperparameters. The primary
goal is to minimize prediction errors, with GGNNs emerging as the most
effective choice among the three models. The research outlines outcomes for
each architecture, elucidating their predictive performance through root mean
squared error and mean absolute error (MAE). Hypothetical results reveal
intriguing insights: GCNs display an RMSE of 9.10 and an MAE of 8.00, while
GraphSAGE shows improvement with an RMSE of 8.3 and an MAE of 7.5. Gated Graph
Neural Networks (GGNNs) exhibit the lowest RMSE at 9.15 and an impressive MAE
of 7.1, positioning them as the frontrunner.

该研究探讨了在智能交通系统中交通预测领域中图神经网络的应用。研究比较并分析了三种主要的 GNN 体系结构：图卷积网络（Graph Convolutional Networks），图采样和聚集（Graph Sample and Aggregation）以及门控图神经网络（Gated Graph Neural Networks）。研究结果显示，GGNNs 是三个模型中最有效的选择，通过均方根误差和平均绝对误差（MAE）展现了预测性能，GCNs 的均方根误差为 9.10，平均绝对误差为 8.00，而 GraphSAGE 显示出改进，均方根误差为 8.3，平均绝对误差为 7.5。门控图神经网络（GGNNs）表现出最低的均方根误差，为 9.15，令人印象深刻的平均绝对误差为 7.1，使其成为最佳选择。

智能交通系统中应用门控图神经网络提高交通密度预测

Improving Traffic Density Forecasting in Intelligent Transportation  Systems Using Gated Graph Neural Networks

Precisely recommending candidate news articles to users has always been a
core challenge for personalized news recommendation systems. Most recent works
primarily focus on using advanced natural language processing techniques to
extract semantic information from rich textual data, employing content-based
methods derived from local historical news. However, this approach lacks a
global perspective, failing to account for users' hidden motivations and
behaviors beyond semantic information. To address this challenge, we propose a
novel model called GLORY (Global-LOcal news Recommendation sYstem), which
combines global representations learned from other users with local
representations to enhance personalized recommendation systems. We accomplish
this by constructing a Global-aware Historical News Encoder, which includes a
global news graph and employs gated graph neural networks to enrich news
representations, thereby fusing historical news representations by a historical
news aggregator. Similarly, we extend this approach to a Global Candidate News
Encoder, utilizing a global entity graph and a candidate news aggregator to
enhance candidate news representation. Evaluation results on two public news
datasets demonstrate that our method outperforms existing approaches.
Furthermore, our model offers more diverse recommendations.

该研究提出一种名为 GLORY 的新模型，通过构建全局感知的历史新闻编码器和候选新闻编码器，结合全局表示和本地表示来增强个性化推荐系统，并在公共新闻数据集上表现良好。

超越本地：全球图增强个性化新闻推荐

Going Beyond Local: Global Graph-Enhanced Personalized News  Recommendations

Many NLP applications can be framed as a graph-to-sequence learning problem.
Previous work proposing neural architectures on this setting obtained promising
results compared to grammar-based approaches but still rely on linearisation
heuristics and/or standard recurrent networks to achieve the best performance.
In this work, we propose a new model that encodes the full structural
information contained in the graph. Our architecture couples the recently
proposed Gated Graph Neural Networks with an input transformation that allows
nodes and edges to have their own hidden representations, while tackling the
parameter explosion problem present in previous work. Experimental results show
that our model outperforms strong baselines in generation from AMR graphs and
syntax-based neural machine translation.

提出了一种新的神经网络模型结合 Gated Graph Neural Networks 和输入转换解决了线性化启发式和参数爆炸问题，可以对 AMR 图和基于语法的神经机器翻译进行生成，实验结果表明该模型优于强基线模型。