Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

Abstract Representation learning over graph structured data has been mostly studied in
static graph settings while efforts for modeling dynamic graphs are still scant. In this paper …

Employing a forward Markov diffusion chain to gradually map the data to a noise distribution,
diffusion probabilistic models learn how to generate the data by inferring a reverse Markov …

We introduce Score identity Distillation (SiD), an innovative data-free method that distills the
generative capabilities of pretrained diffusion models into a single-step generator. SiD not …

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility
of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a …

Graphs with complete node attributes have been widely explored recently. While in practice,
there is a graph where attributes of only partial nodes could be available and those of the …

Generative graph self-supervised learning (SSL) aims to learn node representations by
reconstructing the input graph data. However, most existing methods focus on unsupervised …