Modern sociology has profoundly uncovered many convincing social criteria for behavioral
analysis. Unfortunately, many of them are too subjective to be measured and very …

Graph neural networks (GNNs) have shown prominent performance on attributed network
embedding. However, existing efforts mainly focus on exploiting network structures, while …

The propagation of rumours on social media poses an important threat to societies, so that
various techniques for rumour detection have been proposed recently. Yet, existing work …

Recent advances in data processing have stimulated the demand for learning graphs of very
large scales. Graph neural networks (GNNs), being an emerging and powerful approach in …

Learnable embedding vector is one of the most important applications in machine learning,
and is widely used in various database-related domains. However, the high dimensionality …

Over recent years, denoising diffusion generative models have come to be considered as
state-of-the-art methods for synthetic data generation, especially in the case of generating …

Graph representation learning methods, such as node embeddings, are powerful
approaches to map nodes into a latent vector space, allowing their use for various graph …

Recent advances in data processing have stimulated the demand for learning graphs of very
large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in …

Data-intensive applications dominated by random accesses to large working sets fail to
utilize the computing power of modern processors. Graph random walk, an indispensable …

Node representation learning (NRL) generates numerical vectors (embeddings) for the
nodes of a graph. Structural NRL specifically assigns similar node embeddings for those …