Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

Social network analysis is an important problem in data mining. A fundamental step for
analyzing social networks is to encode network data into low-dimensional representations …

Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …

Due to the flexibility in modelling data heterogeneity, heterogeneous information network
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …

Machine unlearning is a process of removing the impact of some training data from the
machine learning (ML) models upon receiving removal requests. While straightforward and …

Social networks, such as Twitter, form a heterogeneous information network (HIN) where
nodes represent domain entities (eg, user, content, advertiser, etc.) and edges represent …

Abstract Information technology has spread widely, and extraordinarily large amounts of
data have been made accessible to users, which has made it challenging to select data that …

Auto-encoders have emerged as a successful framework for unsupervised learning.
However, conventional auto-encoders are incapable of utilizing explicit relations in …

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 …

Dealing with relational data always required significant computational resources, domain
expertise and task-dependent feature engineering to incorporate structural information into a …