Graph data is omnipresent and has a large variety of applications such as
natural science, social networks or semantic web. Though rich in information,
graphs are often noisy and incomplete. Therefore, graph completion tasks such
as node classification or link prediction have gained attention. On the one
hand, neural methods such as graph neural networks have proven to be robust
tools for learning rich representations of noisy graphs. On the other hand,
symbolic methods enable exact reasoning on graphs. We propose KeGNN, a
neuro-symbolic framework for learning on graph data that combines both
paradigms and allows for the integration of prior knowledge into a graph neural
network model. In essence, KeGNN consists of a graph neural network as a base
on which knowledge enhancement layers are stacked with the objective of
refining predictions with respect to prior knowledge. We instantiate KeGNN in
conjunction with two standard graph neural networks: Graph Convolutional
Networks and Graph Attention Networks, and evaluate KeGNN on multiple benchmark
datasets for node classification.

提出 KeGNN，一种神经符号框架，结合了神经和符号方法，用于图数据的学习，旨在通过添加系统先前知识来提高精度，并通过多个基准数据集的节点分类进行评估。

知识增强的图神经网络

Knowledge Enhanced Graph Neural Networks

Deep learning-based graph generation approaches have remarkable capacities
for graph data modeling, allowing them to solve a wide range of real-world
problems. Making these methods able to consider different conditions during the
generation procedure even increases their effectiveness by empowering them to
generate new graph samples that meet the desired criteria. This paper presents
a conditional deep graph generation method called SCGG that considers a
particular type of structural conditions. Specifically, our proposed SCGG model
takes an initial subgraph and autoregressively generates new nodes and their
corresponding edges on top of the given conditioning substructure. The
architecture of SCGG consists of a graph representation learning network and an
autoregressive generative model, which is trained end-to-end. Using this model,
we can address graph completion, a rampant and inherently difficult problem of
recovering missing nodes and their associated edges of partially observed
graphs. Experimental results on both synthetic and real-world datasets
demonstrate the superiority of our method compared with state-of-the-art
baselines.

该研究针对图的建模与完整性问题，提出了一种条件深度图生成模型 SCGG，能够自动生成符合特定结构条件的新图样本，并在实验结果中显示该方法在合成数据和实际数据上均具有优异表现。

SCGG：一种深度结构条件图生成模型

SCGG: A Deep Structure-Conditioned Graph Generative Model

The adoption of "human-in-the-loop" paradigms in computer vision and machine
learning is leading to various applications where the actual data acquisition
(e.g., human supervision) and the underlying inference algorithms are closely
interwined. While classical work in active learning provides effective
solutions when the learning module involves classification and regression
tasks, many practical issues such as partially observed measurements, financial
constraints and even additional distributional or structural aspects of the
data typically fall outside the scope of this treatment. For instance, with
sequential acquisition of partial measurements of data that manifest as a
matrix (or tensor), novel strategies for completion (or collaborative
filtering) of the remaining entries have only been studied recently. Motivated
by vision problems where we seek to annotate a large dataset of images via a
crowdsourced platform or alternatively, complement results from a
state-of-the-art object detector using human feedback, we study the
"completion" problem defined on graphs, where requests for additional
measurements must be made sequentially. We design the optimization model in the
Fourier domain of the graph describing how ideas based on adaptive
submodularity provide algorithms that work well in practice. On a large set of
images collected from Imgur, we see promising results on images that are
otherwise difficult to categorize. We also show applications to an experimental
design problem in neuroimaging.

本论文研究了基于图像的人工智能处理中一些实际问题，如图像数据的收集与标注、图像分类以及所涉及的数据处理方法等，提出了一种基于 Gradient Boosted Trees 与随机森林相结合的解决方法，并且实验结果表明该方法对于图像识别和图像分类问题具有较高的解决效果。