Deep graph models (e.g., graph neural networks and graph transformers) have
become important techniques for leveraging knowledge across various types of
graphs. Yet, the scaling properties of deep graph models have not been
systematically investigated, casting doubt on the feasibility of achieving
large graph models through enlarging the model and dataset sizes. In this work,
we delve into neural scaling laws on graphs from both model and data
perspectives. We first verify the validity of such laws on graphs, establishing
formulations to describe the scaling behaviors. For model scaling, we
investigate the phenomenon of scaling law collapse and identify overfitting as
the potential reason. Moreover, we reveal that the model depth of deep graph
models can impact the model scaling behaviors, which differ from observations
in other domains such as CV and NLP. For data scaling, we suggest that the
number of graphs can not effectively metric the graph data volume in scaling
law since the sizes of different graphs are highly irregular. Instead, we
reform the data scaling law with the number of edges as the metric to address
the irregular graph sizes. We further demonstrate the reformed law offers a
unified view of the data scaling behaviors for various fundamental graph tasks
including node classification, link prediction, and graph classification. This
work provides valuable insights into neural scaling laws on graphs, which can
serve as an essential step toward large graph models.

通过研究深度图模型的扩展性，本文从模型和数据两个角度揭示了神经扩展定律，并提出了针对图数据的模型和数据扩展定律，为构建大规模图模型提供了重要见解。

图上的神经标度法则

Neural Scaling Laws on Graphs

Recently, table structure recognition has achieved impressive progress with
the help of deep graph models. Most of them exploit single visual cues of
tabular elements or simply combine visual cues with other modalities via early
fusion to reason their graph relationships. However, neither early fusion nor
individually reasoning in terms of multiple modalities can be appropriate for
all varieties of table structures with great diversity. Instead, different
modalities are expected to collaborate with each other in different patterns
for different table cases. In the community, the importance of intra-inter
modality interactions for table structure reasoning is still unexplored. In
this paper, we define it as heterogeneous table structure recognition
(Hetero-TSR) problem. With the aim of filling this gap, we present a novel
Neural Collaborative Graph Machines (NCGM) equipped with stacked collaborative
blocks, which alternatively extracts intra-modality context and models
inter-modality interactions in a hierarchical way. It can represent the
intra-inter modality relationships of tabular elements more robustly, which
significantly improves the recognition performance. We also show that the
proposed NCGM can modulate collaborative pattern of different modalities
conditioned on the context of intra-modality cues, which is vital for
diversified table cases. Experimental results on benchmarks demonstrate our
proposed NCGM achieves state-of-the-art performance and beats other
contemporary methods by a large margin especially under challenging scenarios.

本文提出了基于深度图模型的神经协作图机器（Neural Collaborative Graph Machines，NCGM）用于异构表结构识别，通过堆叠协作块交替提取内部模态上下文，并分层地建模内部 - 内部、内部 - 外部及外部 - 外部间的跨模态关系，从而实现更加鲁棒的表格元素识别效果。通过对比实验，验证了该方法对处理不同种类和多样复杂表格的性能比其他现有方法更优。

神经协同图机器用于表结构识别

Neural Collaborative Graph Machines for Table Structure Recognition

It has been demonstrated that adversarial graphs, i.e., graphs with
imperceptible perturbations added, can cause deep graph models to fail on
node/graph classification tasks. In this paper, we extend adversarial graphs to
the problem of community detection which is much more difficult. We focus on
black-box attack and aim to hide targeted individuals from the detection of
deep graph community detection models, which has many applications in
real-world scenarios, for example, protecting personal privacy in social
networks and understanding camouflage patterns in transaction networks. We
propose an iterative learning framework that takes turns to update two modules:
one working as the constrained graph generator and the other as the surrogate
community detection model. We also find that the adversarial graphs generated
by our method can be transferred to other learning based community detection
models.

本研究扩展有关对付深度图形模型的对抗图的应用，着重研究社区检测这一更为困难的问题，提出了一种基于迭代学习的框架，可以生成具有隐私保护功能的对抗图，并可以在多个基于学习的社区检测模型中传递使用。

通过隐藏个体对社区检测进行对抗攻击

Adversarial Attack on Community Detection by Hiding Individuals

The design of deep graph models still remains to be investigated and the
crucial part is how to explore and exploit the knowledge from different hops of
neighbors in an efficient way. In this paper, we propose a novel RNN-like deep
graph neural network architecture by incorporating AdaBoost into the
computation of network; and the proposed graph convolutional network called
AdaGCN~(Adaboosting Graph Convolutional Network) has the ability to efficiently
extract knowledge from high-order neighbors of current nodes and then
integrates knowledge from different hops of neighbors into the network in an
Adaboost way. Different from other graph neural networks that directly stack
many graph convolution layers, AdaGCN shares the same base neural network
architecture among all ``layers'' and is recursively optimized, which is
similar to an RNN. Besides, We also theoretically established the connection
between AdaGCN and existing graph convolutional methods, presenting the
benefits of our proposal. Finally, extensive experiments demonstrate the
consistent state-of-the-art prediction performance on graphs across different
label rates and the computational advantage of our approach
AdaGCN~\footnote{Code is available at https://github.com/datake/AdaGCN.}

本文提出了一种融合了 AdaBoost 的新型 RNN-like 深度图形神经网络体系结构，该网络能够高效地从当前节点的高阶邻居中提取知识，并以 Adaboost 的方式将不同跳数邻居的知识集成到网络中。通过它与现有图卷积方法的理论联系，我们的 AdaGCN 能够在不同标签率下展现出一致的最先进的预测性能，而且计算效率有所提升。