The advance of deep learning has shown great potential in applications (speech, image,
and video classification). In these applications, deep learning models are trained by …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Huge diagrams have unique properties for organizations and research, such as client
linkages in informal organizations and customer evaluation lattices in social channels. They …

Differentially private analysis of graphs is widely used for releasing statistics from sensitive
graphs while still preserving user privacy. Most existing algorithms however are in a …

This paper studies how to enforce differential privacy by using the randomized response in
the data collection scenario. Given a client's value, the randomized algorithm executed by …

In recent years, deep learning has spread beyond both academia and industry with many
exciting real-world applications. The development of deep learning has presented obvious …

Many real-world networks are inherently decentralized. For example, in social networks,
each user maintains a local view of a social graph, such as a list of friends and her profile. It …

Information networks, such as social media and email networks, often contain sensitive
information. Releasing such network data could seriously jeopardize individual privacy …