Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …

The dynamic neural network (DNN), in contrast to the static counterpart, offers numerous
advantages, such as improved accuracy, efficiency, and interpretability. These benefits stem …

Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of
cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process …

Dynamic network link prediction is extensively applicable in various scenarios, and it has
progressively emerged as a focal point in data mining research. The comprehensive and …

Real-world graphs, such as social networks, financial transactions, and recommendation
systems, often demonstrate dynamic behavior. This phenomenon, known as graph stream …

Recently, self-training strategies are adopted in some entity alignment methods, which
address the scarcity of training data by selecting newly-aligned pairs from the predicted …

Representation learning based on dynamic graphs has received a lot of attention in recent
years due to its wide range of application scenarios. Although many discrete or continuous …

In the realm of cross-network tasks, graph domain adaptation is an effective tool due to its
ability to transfer abundant labels from nodes in the source domain to those in the target …

In recent years, phishing scams have emerged as one of the most serious crimes on
Ethereum. Existing phishing scam detection methods typically model public transaction …

Flow prediction has shown its significance in optimizing and smoothing resource allocation.
However, it still faces challenges due to inherent temporal variability and dynamic spatial …