Anomaly detection in dynamic graphs presents a significant challenge due to
the temporal evolution of graph structures and attributes. The conventional
approaches that tackle this problem typically employ an unsupervised learning
framework, capturing normality patterns with exclusive normal data during
training and identifying deviations as anomalies during testing. However, these
methods face critical drawbacks: they either only depend on proxy tasks for
general representation without directly pinpointing normal patterns, or they
neglect to differentiate between spatial and temporal normality patterns,
leading to diminished efficacy in anomaly detection. To address these
challenges, we introduce a novel Spatial-Temporal memories-enhanced graph
autoencoder (STRIPE). Initially, STRIPE employs Graph Neural Networks (GNNs)
and gated temporal convolution layers to extract spatial features and temporal
features, respectively. Then STRIPE incorporates separate spatial and temporal
memory networks, which capture and store prototypes of normal patterns, thereby
preserving the uniqueness of spatial and temporal normality. After that,
through a mutual attention mechanism, these stored patterns are then retrieved
and integrated with encoded graph embeddings. Finally, the integrated features
are fed into the decoder to reconstruct the graph streams which serve as the
proxy task for anomaly detection. This comprehensive approach not only
minimizes reconstruction errors but also refines the model by emphasizing the
compactness and distinctiveness of the embeddings in relation to the nearest
memory prototypes. Through extensive testing, STRIPE has demonstrated a
superior capability to discern anomalies by effectively leveraging the distinct
spatial and temporal dynamics of dynamic graphs, significantly outperforming
existing methodologies, with an average improvement of 15.39% on AUC values.

通过引入一种新颖的空间 - 时间记忆增强图自编码器 (STRIPE)，该论文解决了动态图中异常检测面临的挑战，通过有效利用动态图的空间和时间动态特性，成功实现了异常识别，并在 AUC 值上取得了 15.39% 的平均改进。

空时记忆增强的图自编码器用于动态图的异常检测

Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly  Detection in Dynamic Graphs

Video anomaly detection is a challenging task in the computer vision
community. Most single task-based methods do not consider the independence of
unique spatial and temporal patterns, while two-stream structures lack the
exploration of the correlations. In this paper, we propose spatial-temporal
memories augmented two-stream auto-encoder framework, which learns the
appearance normality and motion normality independently and explores the
correlations via adversarial learning. Specifically, we first design two proxy
tasks to train the two-stream structure to extract appearance and motion
features in isolation. Then, the prototypical features are recorded in the
corresponding spatial and temporal memory pools. Finally, the encoding-decoding
network performs adversarial learning with the discriminator to explore the
correlations between spatial and temporal patterns. Experimental results show
that our framework outperforms the state-of-the-art methods, achieving AUCs of
98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets.

本文提出了一种增强空间 - 时间记忆的双流自编码器框架，通过对抗学习，学习外观正常性和运动正常性，并探索空间和时间模式之间的相关性，在 UCSD Ped2 和 CUHK Avenue 数据集上，该框架的 AUC 分别为 98.1％和 89.8％，胜过最先进的方法。