In recent years, semi-supervised graph learning with data augmentation (DA)
is currently the most commonly used and best-performing method to enhance model
robustness in sparse scenarios with few labeled samples. Differing from
homogeneous graph, DA in heterogeneous graph has greater challenges:
heterogeneity of information requires DA strategies to effectively handle
heterogeneous relations, which considers the information contribution of
different types of neighbors and edges to the target nodes. Furthermore,
over-squashing of information is caused by the negative curvature that formed
by the non-uniformity distribution and strong clustering in complex graph. To
address these challenges, this paper presents a novel method named
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
(HG-MDA). For the problem of heterogeneity of information in DA, node and
topology augmentation strategies are proposed for the characteristics of
heterogeneous graph. And meta-relation-based attention is applied as one of the
indexes for selecting augmented nodes and edges. For the problem of
over-squashing of information, triangle based edge adding and removing are
designed to alleviate the negative curvature and bring the gain of topology.
Finally, the loss function consists of the cross-entropy loss for labeled data
and the consistency regularization for unlabeled data. In order to effectively
fuse the prediction results of various DA strategies, the sharpening is used.
Existing experiments on public datasets, i.e., ACM, DBLP, OGB, and industry
dataset MB show that HG-MDA outperforms current SOTA models. Additionly, HG-MDA
is applied to user identification in internet finance scenarios, helping the
business to add 30% key users, and increase loans and balances by 3.6%, 11.1%,
and 9.8%.

该论文提出了一种名为 HG-MDA 的半监督异质图学习方法，通过数据增强、节点和拓扑增强等策略处理稀疏数据场景下异质图的信息异质性问题，采用基于元关系的关注机制和三角形边添加、删除等方式解决了负曲率问题，应用于互联网金融场景下的用户识别任务，取得了比当前 SOTA 模型更好的效果。

半监督异构图学习与多层数据增广

Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation

In recent years, many techniques have been developed to improve the
performance and efficiency of data center networks. While these techniques
provide high accuracy, they are often designed using heuristics that leverage
domain-specific properties of the workload or hardware.
In this vision paper, we argue that many data center networking techniques,
e.g., routing, topology augmentation, energy savings, with diverse goals
actually share design and architectural similarity. We present a design for
developing general intermediate representations of network topologies using
deep learning that is amenable to solving classes of data center problems. We
develop a framework, DeepConfig, that simplifies the processing of configuring
and training deep learning agents that use the intermediate representation to
learns different tasks. To illustrate the strength of our approach, we
configured, implemented, and evaluated a DeepConfig-Agent that tackles the data
center topology augmentation problem. Our initial results are promising ---
DeepConfig performs comparably to the optimal.

本文提出利用深度学习方法，设计通用中间表示法以解决数据中心网络中的多种问题，并开发了一个框架 DeepConfig，该框架简化了配置和训练深度学习代理的过程，并表明其在数据中心拓扑增强问题上表现优异。