Node classification tasks aim to assign labels or categories to entire graphs based on their
structural properties or node attributes. It can be adopted for various types of graph systems …

Real-world information networks are increasingly occurring across various disciplines
including online social networks and citation networks. These network data are generally …

Fraud detection is one of the most important tasks in Web platforms such as e-commerce,
social media, network security, and financial systems. To prevent fraudulent actions from …

Public risk perception is the psychological perception and cognitive behavior towards public
health emergencies, the high level of which can seriously threaten mental health and social …

Network embedding is a fundamental part of many network analysis tasks, including node
classification and link prediction. The existing random walk-based embedding methods aim …

Recommender systems aim to capture users' personalized preferences from the cast
amount of user behaviors, making them pivotal in the era of information explosion. However …

Network embedding aims to learn a low-dimensional vector for each node in networks,
which is effective in a variety of applications such as network reconstruction and community …

Graph embedding provides an effective way to encode graph nodes as continuous vector
representations in a low-dimensional space. The high-order proximities based graph …

The network representation learning fusing multidimensional classification information of
nodes aims to effectively combine nodes multi-dimensional classification information and …

Many network-based tasks need powerful feature expression to capture the diversity of
networks. It can be provided by network embedding learning of nodes. The related …