Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

Learning on Graphs has attracted immense attention due to its wide real-world applications.
The most popular pipeline for learning on graphs with textual node attributes primarily relies …

Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …

The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …

We introduce S2ORC, a large corpus of 81.1 M English-language academic papers
spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts …

Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …

Methods that learn representations of nodes in a graph play a critical role in network
analysis since they enable many downstream learning tasks. We propose Graph2Gauss-an …

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …