Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it
is susceptible to low-quality and unreliable structure, which has been a norm rather than an …

Traffic forecasting is a complex multivariate time-series regression task of paramount
importance for traffic management and planning. However, existing approaches often …

The importance of effective detection is underscored by the fact that socialbots imitate
human behavior to propagate misinformation, leading to an ongoing competition between …

Skin lesion segmentation is a fundamental task in dermoscopic image analysis. The
complex features of pixels in the lesion region impede the lesion segmentation accuracy …

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 …

In recent years, advanced machine learning and artificial intelligence techniques have
gained popularity due to their ability to solve problems across various domains with high …

State abstraction optimizes decision-making by ignoring irrelevant environmental
information in reinforcement learning with rich observations. Nevertheless, recent …

Graph kernels have been regarded as a successful tool for handling a variety of graph
applications since they were proposed. However, most of the proposed graph kernels are …

Structural entropy is a metric that measures the amount of information embedded in graph
structure data under a strategy of hierarchical abstracting. To measure the structural entropy …