The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

The misuse of large language models (LLMs) has drawn significant attention from the
general public and LLM vendors. One particular type of adversarial prompt, known 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 …

Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …

We present DeepWalk, a novel approach for learning latent representations of vertices in a
network. These latent representations encode social relations in a continuous vector space …

Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

Abstract Distrust in scientific expertise,,,,,,,,,,,,–is dangerous. Opposition to vaccination with a
future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could …

The human brain is a complex system whose topological organization can be represented
using connectomics. Recent studies have shown that human connectomes can be …

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and
continuous cell transitions. Partition-based graph abstraction (PAGA) provides an …