Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-
stationary, leading to the continuous challenge of spatial-temporal graph learning. In the …

Research on social bot detection plays a crucial role in maintaining the order and reliability
of information dissemination while increasing trust in social interactions. The current …

Unsupervised anomaly detection in time series is essential in industrial applications, as it
significantly reduces the need for manual intervention. Multivariate time series pose a …

Recently, there has been a growing interest in predicting human motion, which involves
forecasting future body poses based on observed pose sequences. This task is complex due …

Traffic demand prediction plays a crucial role in developing modern transport systems, as it
can alleviate the dilemma of demand-and-supply imbalances in urban traffic. However, most …

Structural Entropy (SE) measures the structural information contained in a graph. Minimizing
or maximizing SE helps to reveal or obscure the intrinsic structural patterns underlying …

Semi-supervised clustering techniques have emerged as valuable tools for leveraging prior
information in the form of constraints to improve the quality of clustering outcomes. Despite …

Link prediction, as a fundamental task in dynamic networks, holds great developmental
significance. Dynamic networks contain rich spatiotemporal features which are crucial for …

This paper introduces CABGSI, a novel graph-based clustering algorithm that effectively
addresses the limitations of traditional clustering techniques. Unlike conventional methods …

The long-term time series forecasting (LTSF) plays a crucial role in various domains, utilizing
a large amount of historical data to forecast trends over an extended future time range …