Researchers have collected Twitter data to study a wide range of topics. This growing body
of literature, however, has not yet been reviewed systematically to synthesize Twitter-related …

Networks (or graphs) appear as dominant structures in diverse domains, including
sociology, biology, neuroscience and computer science. In most of the aforementioned …

Abstract We introduce Graph of Thoughts (GoT): a framework that advances prompting
capabilities in large language models (LLMs) beyond those offered by paradigms such as …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

This paper studies the expressive power of graph neural networks falling within the
message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown …

We propose NetGAN-the first implicit generative model for graphs able to mimic real-world
networks. We pose the problem of graph generation as learning the distribution of biased …

Graphs are a fundamental abstraction for modeling relational data. However, graphs are
discrete and combinatorial in nature, and learning representations suitable for machine …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

Diffusion-based generative graph models have been proven effective in generating high-
quality small graphs. However, they need to be more scalable for generating large graphs …

Randomized algorithms for very large matrix problems have received a great deal of
attention in recent years. Much of this work was motivated by problems in large-scale data …