Node embedding algorithms produce low-dimensional latent representations of
nodes in a graph. These embeddings are often used for downstream tasks, such as
node classification and link prediction. In this paper, we investigate the
following two questions: (Q1) Can we explain each embedding dimension with
human-understandable graph features (e.g. degree, clustering coefficient and
PageRank). (Q2) How can we modify existing node embedding algorithms to produce
embeddings that can be easily explained by human-understandable graph features?
We find that the answer to Q1 is yes and introduce a new framework called XM
(short for eXplain eMbedding) to answer Q2. A key aspect of XM involves
minimizing the nuclear norm of the generated explanations. We show that by
minimizing the nuclear norm, we minimize the lower bound on the entropy of the
generated explanations. We test XM on a variety of real-world graphs and show
that XM not only preserves the performance of existing node embedding methods,
but also enhances their explainability.

研究论文通过分析图中的节点嵌入算法和人可理解的图特征的关系，提出了一种叫做 XM 的新框架，该框架通过最小化核范数来生成能够解释的嵌入，既保持了现有节点嵌入方法的性能，又增强了其可解释性。

生成节点嵌入的人类可理解解释

Generating Human Understandable Explanations for Node Embeddings

Advances in Visually Rich Document Understanding (VrDU) have enabled
information extraction and question answering over documents with complex
layouts. Two tropes of architectures have emerged -- transformer-based models
inspired by LLMs, and Graph Neural Networks. In this paper, we introduce
DocGraphLM, a novel framework that combines pre-trained language models with
graph semantics. To achieve this, we propose 1) a joint encoder architecture to
represent documents, and 2) a novel link prediction approach to reconstruct
document graphs. DocGraphLM predicts both directions and distances between
nodes using a convergent joint loss function that prioritizes neighborhood
restoration and downweighs distant node detection. Our experiments on three
SotA datasets show consistent improvement on IE and QA tasks with the adoption
of graph features. Moreover, we report that adopting the graph features
accelerates convergence in the learning process during training, despite being
solely constructed through link prediction.

该研究论文介绍了一种名为 DocGraphLM 的新型框架，它结合了预训练语言模型和图形语义，通过使用联合编码器架构来表示文档，并采用一种新颖的链接预测方法来重构文档图。DocGraphLM 通过采用图形特征，在信息提取和问答任务中持续改善了目标性能，并且提供了在训练过程中加速收敛的优点。

DocGraphLM: 信息提取的文档图谱语言模型

DocGraphLM: Documental Graph Language Model for Information Extraction

A graph is a very common and powerful data structure used for modeling
communication and social networks. Models that generate graphs with arbitrary
features are important basic technologies in repeated simulations of networks
and prediction of topology changes. Although existing generative models for
graphs are useful for providing graphs similar to real-world graphs, graph
generation models with tunable features have been less explored in the field.
Previously, we have proposed GraphTune, a generative model for graphs that
continuously tune specific graph features of generated graphs while maintaining
most of the features of a given graph dataset. However, the tuning accuracy of
graph features in GraphTune has not been sufficient for practical applications.
In this paper, we propose a method to improve the accuracy of GraphTune by
adding a new mechanism to feed back errors of graph features of generated
graphs and by training them alternately and independently. Experiments on a
real-world graph dataset showed that the features in the generated graphs are
accurately tuned compared with conventional models.

通过向 GraphTune 模型中添加新机制并进行独立的交替训练，提高了图形特征调节的准确性。实验结果表明，与传统模型相比，生成的图形的特征得到了准确调节。

一种具有可调特征的精准图生成模型

An Accurate Graph Generative Model with Tunable Features

We explore the generalization of scattering transforms from traditional
(e.g., image or audio) signals to graph data, analogous to the generalization
of ConvNets in geometric deep learning, and the utility of extracted graph
features in graph data analysis. In particular, we focus on the capacity of
these features to retain informative variability and relations in the data
(e.g., between individual graphs, or in aggregate), while relating our
construction to previous theoretical results that establish the stability of
similar transforms to families of graph deformations. We demonstrate the
application the our geometric scattering features in graph classification of
social network data, and in data exploration of biochemistry data.

本篇论文探讨了散射变换从传统（如图像或音频）信号到图数据的归纳推广，类似于 geometric deep learning 中 ConvNets 的归纳推广，并研究了提取的图特征在图数据分析中的实用性，尤其关注这些特征保留数据中的信息变量和关联的能力，同时将我们的构建与之前的一些理论结果联系起来，这些结果建立在类似变换到图变形的家族上的稳定性上。 我们证明了在社交网络数据的图分类和生物化学数据的数据探索中应用了我们的几何散射特征。