Complex network has been a powerful tool for time series analysis by encoding dynamical
temporal information in network topology. In this article, we introduce a framework to build a …

In the field of graph neural networks (GNNs) for representation learning, a noteworthy
highlight is the potential of embedding fusion architectures for augmented graphs. However …

The mainstream approach of GNN-based recommendation aggregates high-order ID
information associated with the node in the user-item graph. The aggregation pattern using …

Image denoising is a quintessential challenge in computer vision, intending to produce high-
quality, clean images from degraded, noisy counterparts. Infrared imaging holds a pivotal …

Change point detection in dynamic networks aims to detect the points of sudden change or
abnormal events within the network. It has garnered substantial interest from researchers …

Graph neural networks (GNNs) have demonstrated exceptional performance in processing
various types of graph data, such as citation networks and social networks, etc. Although …

Identifying anomalies in multi-dimensional sequential data is crucial for ensuring optimal
performance across various domains and in large-scale systems. Traditional contrastive …

Abstract Knowledge tracing (KT) aims to predict students' future performance by tracking
their learning behaviors in intelligent tutoring systems (ITS). In KT, three main types of …

Graph neural networks (GNNs) have achieved considerable success in dealing with graph-
structured data by the message-passing mechanism. Actually, this mechanism relies on a …

Abstract Graph Transformers (GTs) have achieved impressive results on various graph-
related tasks. However, the huge computational cost of GTs hinders their deployment and …