Attributed graph anomaly detection aims to identify abnormal nodes that significantly differ
from most nodes in terms of their attribute or structure. Recent graph contrastive learning …

Learning expressive representations for high-dimensional yet sparse features has been a
longstanding problem in information retrieval. Though recent deep learning methods can …

Large-scale, high-quality data are considered an essential factor for the successful
application of many deep learning techniques. Meanwhile, numerous real-world deep …

Graph convolutional networks (GCNs) are widely believed to perform well in the graph node
classification task, and homophily assumption plays a core rule in the design of previous …

Graphs present in many real-world applications, such as financial fraud detection,
commercial recommendation, and social network analysis. But given the high cost of graph …

针对交通流预测模型没有考虑道路上下文相关性和空间依赖关系动态性的问题,
提出一种基于混合图卷积的多通道时空交通流预测模型(MHGCN). 该模型采用三明治结构(即 …

Generalizable and transferrable graph representation learning endows graph neural
networks (GNN) with the ability to extrapolate potential test distributions. Nonetheless …

Graph learning is widely applied to process various complex data structures (eg, time series)
in different domains. Due to multidimensional observations and the requirement for accurate …

Graph contrastive learning (GCL) has emerged as a successful method for self-supervised
graph learning. It involves generating augmented views of a graph by augmenting its edges …

Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these
classes in model training. The ZSL has been widely used in many knowledge-based models …