Advancements in information and communication technology, and online web users have
given attention to the virtual representation of each user, which is crucial for effective service …

Existing embedding methods for attributed networks aim at learning low-dimensional vector
representations for nodes only but not for both nodes and attributes, resulting in the fact that …

Aiming to represent user characteristics and personal interests, the task of user profiling is
playing an increasingly important role for many real-world applications, eg, e-commerce and …

Real-world networks are composed of diverse interacting and evolving entities, while most
of existing researches simply characterize them as particular static networks, without …

Temporal attributed network embedding aspires to learn a low-dimensional vector
representation for each node in each snapshot of a temporal network, which can be capable …

In the era of User Generated Content (UGC), authors (IDs) of texts widely exist and play a
key role in determining the topic categories of texts. Existing text clustering efforts are mainly …

In this article, we study the problem of embedding temporal attributed networks, with the goal
of which is to learn dynamic low-dimensional representations over time for temporal …

Deep generative models (DGMs) have achieved remarkable advances. Semi-supervised
variational auto-encoders (SVAE) as a classical DGM offers a principled framework to …

The rapid development of e-commerce gives researchers confidence that customers will be
willing to share more and more online data, which in turn, would allow for improved mining …

This article introduces a novel architecture for two objectives recommendation and
interpretability in a unified model. We leverage textual content as a source of interpretability …