Recent advancements in social bot detection have been driven by the adoption of Graph
Neural Networks. The social graph, constructed from social network interactions, contains …

Abstract Physics-informed Graph Neural Networks have achieved remarkable performance
in learning through graph-structured data by mitigating common GNN challenges such as …

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 …

In recent years, the utilization of deep models has significantly enhanced the performance of
neural machine translation (NMT). Nevertheless, the uneven distribution of data leads to …

With the rapid growth of data volumes in real-world applications, anomaly detection has
become a crucial task across various scenarios. Anomalies are generally defined as data …

Due to the rapid spread of rumors on social media, which has a detrimental effect on our
lives, it is becoming increasingly important to detect rumors. It has been proved that the …

Traditional information theory provides a valuable foundation for Reinforcement Learning,
particularly through representation learning and entropy maximization for agent exploration …

Abstract Product Knowledge Graphs (PKGs) play a crucial role in enhancing e-commerce
system performance by providing structured information about entities and their …

Heterogeneous graph neural networks have attracted considerable attention for their
proficiency in handling intricate heterogeneous structures. However, most existing methods …

As a crucial preprocessing step in data mining, feature selection aims to obtain an excellent
feature set, so as to improve the accuracy of classifiers and reduce the training time. This …