Recent progress in research on Deep Graph Networks (DGNs) has led to a
maturation of the domain of learning on graphs. Despite the growth of this
research field, there are still important challenges that are yet unsolved.
Specifically, there is an urge of making DGNs suitable for predictive tasks on
realworld systems of interconnected entities, which evolve over time. With the
aim of fostering research in the domain of dynamic graphs, at first, we survey
recent advantages in learning both temporal and spatial information, providing
a comprehensive overview of the current state-of-the-art in the domain of
representation learning for dynamic graphs. Secondly, we conduct a fair
performance comparison among the most popular proposed approaches, leveraging
rigorous model selection and assessment for all the methods, thus establishing
a sound baseline for evaluating new architectures and approaches

本文通过对时空信息学习的先进优势进行调查和性能比较，为动态图形表示学习领域建立坚实的基础，以促进该领域的研究

动态图的深度学习：模型与基准

Deep learning for dynamic graphs: models and benchmarks

Learning on Graphs has attracted immense attention due to its wide real-world
applications. The most popular pipeline for learning on graphs with textual
node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes
shallow text embedding as initial node representations, which has limitations
in general knowledge and profound semantic understanding. In recent years,
Large Language Models (LLMs) have been proven to possess extensive common
knowledge and powerful semantic comprehension abilities that have
revolutionized existing workflows to handle text data. In this paper, we aim to
explore the potential of LLMs in graph machine learning, especially the node
classification task, and investigate two possible pipelines: LLMs-as-Enhancers
and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text
attributes with their massive knowledge and then generate predictions through
GNNs. The latter attempts to directly employ LLMs as standalone predictors. We
conduct comprehensive and systematical studies on these two pipelines under
various settings. From comprehensive empirical results, we make original
observations and find new insights that open new possibilities and suggest
promising directions to leverage LLMs for learning on graphs.

本文旨在探索使用大型语言模型（LLMs）进行图机器学习的潜力，特别是节点分类任务，并调查两种潜在的流程：LLMs 作为增强器和 LLMs 作为预测器。通过全面而系统的研究，我们获得了原创性观察并发现新的见解，为利用 LLMs 进行图学习提供了新的可能性和有希望的方向。

探索大型语言模型在图学习中的潜力

Exploring the Potential of Large Language Models (LLMs) in Learning on  Graphs

Learning on graphs, where instance nodes are inter-connected, has become one
of the central problems for deep learning, as relational structures are
pervasive and induce data inter-dependence which hinders trivial adaptation of
existing approaches that assume inputs to be i.i.d.~sampled. However, current
models mostly focus on improving testing performance of in-distribution data
and largely ignore the potential risk w.r.t. out-of-distribution (OOD) testing
samples that may cause negative outcome if the prediction is overconfident on
them. In this paper, we investigate the under-explored problem, OOD detection
on graph-structured data, and identify a provably effective OOD discriminator
based on an energy function directly extracted from graph neural networks
trained with standard classification loss. This paves a way for a simple,
powerful and efficient OOD detection model for GNN-based learning on graphs,
which we call GNNSafe. It also has nice theoretical properties that guarantee
an overall distinguishable margin between the detection scores for
in-distribution and OOD samples, which, more critically, can be further
strengthened by a learning-free energy belief propagation scheme. For
comprehensive evaluation, we introduce new benchmark settings that evaluate the
model for detecting OOD data from both synthetic and real distribution shifts
(cross-domain graph shifts and temporal graph shifts). The results show that
GNNSafe achieves up to $17.0\%$ AUROC improvement over state-of-the-arts and it
could serve as simple yet strong baselines in such an under-developed area.

针对图形数据的 OOD 检测问题，本论文提出了一种基于图神经网络的能量函数的有效 OOD 鉴别器，命名为 GNNSafe，并在模拟和真实数据集上的评估中证明了它的性能优于现有技术。

基于能量的图神经网络异常检测

Energy-based Out-of-Distribution Detection for Graph Neural Networks

We introduce a new convex optimization problem, termed quadratic decomposable
submodular function minimization. The problem is closely related to
decomposable submodular function minimization and arises in many learning on
graphs and hypergraphs settings, such as graph-based semi-supervised learning
and PageRank. We approach the problem via a new dual strategy and describe an
objective that may be optimized via random coordinate descent (RCD) methods and
projections onto cones. We also establish the linear convergence rate of the
RCD algorithm and develop efficient projection algorithms with provable
performance guarantees. Numerical experiments in semi-supervised learning on
hypergraphs confirm the efficiency of the proposed algorithm and demonstrate
the significant improvements in prediction accuracy with respect to
state-of-the-art methods.

本文提出了一种新的凸优化问题 —— 二次可分解子模函数极小化问题，该问题与可分解子模函数极小化问题密切相关，并在许多基于图和超图的学习环境中产生，例如基于图的半监督学习和 PageRank。我们采用了一种新的解决策略，并描述了可以通过随机坐标下降（RCD）方法和锥体投影优化的目标。我们还确定了 RCD 算法的线性收敛率，并开发了具有可证明性能保证的高效投影算法。基于超图的半监督学习的数字实验证实了所提出算法的效率，并证明了与现有最先进方法相比，在预测精度方面取得了显着的改进。