Examining the particular value of each platform for big data would be difficult because of the
variety of social media forms and sizes. Using social media to objectively and subjectively …

Source localization, as a reverse problem of the graph diffusion, bears paramount
significance for a multitude of applications, such as tracking social rumors, detecting …

Graph neural networks (GNNs) have gained significant attention for their impressive results
on different graph-based tasks. The essential mechanism of GNNs is the message-passing …

The massive amounts of online user behavior data on social networks allow for the
investigation of information diffusion prediction, which is essential to comprehend how …

The identification of opinion leaders is a matter of great significance for companies and
authorities, as these individuals are able to shape the opinions and attitudes of entire …

Graph Neural Networks (GNNs) have gained significant popularity for learning
representations of graph-structured data. Mainstream GNNs employ the message passing …

Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the
majority in a graph without the aid of labels, which is important for a variety of real-world …

Q&A platforms are vital sources of information but often face challenges related to their high
ratios of passive to active contributors, which can impede knowledge construction and …

With the exponential growth of social media platforms, retweet behavior has become a
crucial factor in various social network applications like message diffusion, business …

Graph neural networks (GNNs) have been widely employed in various fields including
knowledge graphs and social networks. When dealing with large-scale graphs, traditional …