Knowledge graphs have emerged as an effective tool for managing and
standardizing semistructured domain knowledge in a human- and
machine-interpretable way. In terms of graph-based domain applications, such as
embeddings and graph neural networks, current research is increasingly taking
into account the time-related evolution of the information encoded within a
graph. Algorithms and models for stationary and static knowledge graphs are
extended to make them accessible for time-aware domains, where time-awareness
can be interpreted in different ways. In particular, a distinction needs to be
made between the validity period and the traceability of facts as objectives of
time-related knowledge graph extensions. In this context, terms and definitions
such as dynamic and temporal are often used inconsistently or interchangeably
in the literature. Therefore, with this paper we aim to provide a short but
well-defined overview of time-aware knowledge graph extensions and thus
faciliate future research in this field as well.

该论文旨在提供时间感知知识图扩展的简要但定义明确的概述，以促进该领域的未来研究。

关于一种针对时间感知知识图谱的广义框架

On a Generalized Framework for Time-Aware Knowledge Graphs

Graph mining algorithms have been playing a significant role in myriad fields
over the years. However, despite their promising performance on various graph
analytical tasks, most of these algorithms lack fairness considerations. As a
consequence, they could lead to discrimination towards certain populations when
exploited in human-centered applications. Recently, algorithmic fairness has
been extensively studied in graph-based applications. In contrast to
algorithmic fairness on independent and identically distributed (i.i.d.) data,
fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling
techniques. In this survey, we provide a comprehensive and up-to-date
introduction of existing literature under the context of fair graph mining.
Specifically, we propose a novel taxonomy of fairness notions on graphs, which
sheds light on their connections and differences. We further present an
organized summary of existing techniques that promote fairness in graph mining.
Finally, we summarize the widely used datasets in this emerging research field
and provide insights on current research challenges and open questions, aiming
at encouraging cross-breeding ideas and further advances.

本文介绍了公平图挖掘领域中现有文献的全面和最新研究进展，提出了基于图的公平法则的新分类方法，并提出了促进公平性的现有技术的有组织的总结。还总结了该领域中广泛使用的数据集，并提供了对当前研究挑战和开放问题的见解，以鼓励跨越交流思想和进一步发展。

图挖掘中的公平性：一项调查

Fairness in Graph Mining: A Survey

Graph Augmentation Learning (GAL) provides outstanding solutions for graph
learning in handling incomplete data, noise data, etc. Numerous GAL methods
have been proposed for graph-based applications such as social network analysis
and traffic flow forecasting. However, the underlying reasons for the
effectiveness of these GAL methods are still unclear. As a consequence, how to
choose optimal graph augmentation strategy for a certain application scenario
is still in black box. There is a lack of systematic, comprehensive, and
experimentally validated guideline of GAL for scholars. Therefore, in this
survey, we in-depth review GAL techniques from macro (graph), meso (subgraph),
and micro (node/edge) levels. We further detailedly illustrate how GAL enhance
the data quality and the model performance. The aggregation mechanism of
augmentation strategies and graph learning models are also discussed by
different application scenarios, i.e., data-specific, model-specific, and
hybrid scenarios. To better show the outperformance of GAL, we experimentally
validate the effectiveness and adaptability of different GAL strategies in
different downstream tasks. Finally, we share our insights on several open
issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability,
and generalization.

通过针对宏观、中观和微观层面的图的加强策略及其对数据质量和模型效果的改善作详细阐述，并着重讨论了针对数据特定场景、模型特定场景和混合场景的不同应用场景下的聚合机制和学习模型优化策略相结合的优化方案，从而验证了 GAL 在各种下游任务中不同策略的有效性和适应性。最后，作者还提出了 GAL 的异构性、时空动态性、可扩展性和泛化性等几个需要进一步解决的问题。