There are vast number of configurable parameters in a Radio Access Telecom
Network. A significant amount of these parameters is configured by Radio Node
or cell based on their deployment setting. Traditional methods rely on domain
knowledge for individual parameter configuration, often leading to sub-optimal
results. To improve this, a framework using a Deep Generative Graph Neural
Network (GNN) is proposed. It encodes the network into a graph, extracts
subgraphs for each RAN node, and employs a Siamese GNN (S-GNN) to learn
embeddings. The framework recommends configuration parameters for a multitude
of parameters and detects misconfigurations, handling both network expansion
and existing cell reconfiguration. Tested on real-world data, the model
surpasses baselines, demonstrating accuracy, generalizability, and robustness
against concept drift.

使用深度生成图神经网络 (GNN) 框架对无线电接入电信网络中的可配置参数进行优化和检测，提高配置精确度，并处理网络扩展和现有单元重配置产生的错误配置。

使用深层生成图神经网络进行移动网络配置推荐

Mobile Network Configuration Recommendation using Deep Generative Graph  Neural Network

A molecule's geometry, also known as conformation, is one of a molecule's
most important properties, determining the reactions it participates in, the
bonds it forms, and the interactions it has with other molecules. Conventional
conformation generation methods minimize hand-designed molecular force field
energy functions that are often not well correlated with the true energy
function of a molecule observed in nature. They generate geometrically diverse
sets of conformations, some of which are very similar to the lowest-energy
conformations and others of which are very different. In this paper, we propose
a conditional deep generative graph neural network that learns an energy
function by directly learning to generate molecular conformations that are
energetically favorable and more likely to be observed experimentally in
data-driven manner. On three large-scale datasets containing small molecules,
we show that our method generates a set of conformations that on average is far
more likely to be close to the corresponding reference conformations than are
those obtained from conventional force field methods. Our method maintains
geometrical diversity by generating conformations that are not too similar to
each other, and is also computationally faster. We also show that our method
can be used to provide initial coordinates for conventional force field
methods. On one of the evaluated datasets we show that this combination allows
us to combine the best of both methods, yielding generated conformations that
are on average close to reference conformations with some very similar to
reference conformations.

本文提出了一种基于条件深度生成图神经网络的分子构像生成方法，可以在数据驱动的基础上直接学习生成符合能量有利的、更可能在实验中被观察到的分子构像，相较于传统力场方法生成的构像更加接近于参考构像，并且保持着几何多样性，可以提供传统力场方法的初始坐标。