Anomaly detection (such as telecom fraud detection and medical image
detection) has attracted the increasing attention of people. The complex
interaction between multiple entities widely exists in the network, which can
reflect specific human behavior patterns. Such patterns can be modeled by
higher-order network structures, thus benefiting anomaly detection on
attributed networks. However, due to the lack of an effective mechanism in most
existing graph learning methods, these complex interaction patterns fail to be
applied in detecting anomalies, hindering the progress of anomaly detection to
some extent. In order to address the aforementioned issue, we present a
higher-order structure based anomaly detection (GUIDE) method. We exploit
attribute autoencoder and structure autoencoder to reconstruct node attributes
and higher-order structures, respectively. Moreover, we design a graph
attention layer to evaluate the significance of neighbors to nodes through
their higher-order structure differences. Finally, we leverage node attribute
and higher-order structure reconstruction errors to find anomalies. Extensive
experiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and
Pubmed) are implemented to verify the effectiveness of GUIDE. Experimental
results in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly
outperforms the state-of-art methods.

我们提出了一种基于高阶结构的异常检测方法（GUIDE），利用属性自编码器和结构自编码器对节点属性和高阶结构进行重建，并通过图注意力层评估邻居节点对节点的重要性，最终利用节点属性和高阶结构重建误差来寻找异常。在五个真实数据集上进行了广泛的实验，实验结果表明，GUIDE 在 ROC-AUC、PR-AUC 和 Recall@K 等指标上明显优于现有方法。

基于高阶结构的属性网络异常检测

Higher-order Structure Based Anomaly Detection on Attributed Networks

In the wake of the growing popularity of machine learning in particle
physics, this work finds a new application of geometric deep learning on
Feynman diagrams to make accurate and fast matrix element predictions with the
potential to be used in analysis of quantum field theory. This research uses
the graph attention layer which makes matrix element predictions to 1
significant figure accuracy above 90% of the time. Peak performance was
achieved in making predictions to 3 significant figure accuracy over 10% of the
time with less than 200 epochs of training, serving as a proof of concept on
which future works can build upon for better performance. Finally, a procedure
is suggested, to use the network to make advancements in quantum field theory
by constructing Feynman diagrams with effective particles that represent
non-perturbative calculations.

使用几何深度学习和图注意力层，本研究在费曼图上实现了准确且快速的基元矩阵预测，证明该方法在量子场论分析中具有潜在应用，可构建代表非微扰计算的有效粒子费曼图，为量子场论的研究提供了突破。

利用图神经网络学习费曼图

Learning Feynman Diagrams using Graph Neural Networks

The problem of predicting human motion given a sequence of past observations
is at the core of many applications in robotics and computer vision. Current
state-of-the-art formulate this problem as a sequence-to-sequence task, in
which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that
predicts future movements, typically in the order of 1 to 2 seconds. However,
one aspect that has been obviated so far, is the fact that human motion is
inherently driven by interactions with objects and/or other humans in the
environment. In this paper, we explore this scenario using a novel
context-aware motion prediction architecture. We use a semantic-graph model
where the nodes parameterize the human and objects in the scene and the edges
their mutual interactions. These interactions are iteratively learned through a
graph attention layer, fed with the past observations, which now include both
object and human body motions. Once this semantic graph is learned, we inject
it to a standard RNN to predict future movements of the human/s and object/s.
We consider two variants of our architecture, either freezing the contextual
interactions in the future of updating them. A thorough evaluation in the
"Whole-Body Human Motion Database" shows that in both cases, our context-aware
networks clearly outperform baselines in which the context information is not
considered.

本文利用语义图模型和图注意力层来建立上下文感知体系结构，旨在正确建模人类行为中的对象和人际互动以提高人类运动的预测准确性。通过在全身人类动作数据库上进行的彻底评估，论文显示出模式的优越性。

上下文感知的人体动作预测

Context-aware Human Motion Prediction

Estimating multiple attributes from a single facial image gives comprehensive
descriptions on the high level semantics of the face. It is naturally regarded
as a multi-task supervised learning problem with a single deep CNN, in which
lower layers are shared, and higher ones are task-dependent with the
multi-branch structure. Within the traditional deep multi-task learning (DMTL)
framework, this paper intends to fully exploit the correlations among different
attributes by constructing a graph. The node in graph represents the feature
vector from a particular branch for a given attribute, and the edge can be
defined by either the prior knowledge or the similarity between two nodes in
the embedding with a fully data-driven manner. We analyze that the attention
mechanism actually takes effect in the latter case, and utilize the Graph
Attention Layer (GAL) for exploring on the most relevant attribute feature and
refining the task-dependant feature by considering other attributes.
Experiments show that by mining the correlations among attributes, our method
can improve the recognition accuracy on CelebA and LFWA dataset. And it also
achieves competitive performance.

本文提出一种基于多任务学习和图注意力层的人脸多属性识别方法，通过对属性关系的挖掘来提高 CelebA 和 LFWA 数据集上的识别准确率。