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

In recent years, prompt tuning has sparked a research surge in adapting pre-trained models.
Unlike the unified pre-training strategy employed in the language field, the graph field …

Network modeling, from the ecological to the molecular scale has become an essential tool
for studying the structure, dynamics and complex behavior of living systems. Graph …

Graph is a universe data structure that is widely used to organize data in real-world. Various
real-word networks like the transportation network, social and academic network can be …

Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk
factors, principally taking place in metabolic organs and the arterial wall. Here we show that …

The self-supervised graph representation learning has achieved much success in recent
web based research and applications, such as recommendation system, social networks …

The genetics of phenotypic responses to changing environments remains elusive. Using
whole-genome quantitative gene expression as a model, here we study how the genetic …

The ubiquitous daily rhythms in mammalian physiology are guided by progression of the
circadian clock. In mice, systemic disruption of the clock can promote tumor growth. In vitro …

In recent years, prompt tuning has set off a research boom in the adaptation of pre-trained
models. In this paper, we propose Graph Prompt as an efficient and effective alternative to …

In recent years, graph pre-training has gained significant attention, focusing on acquiring
transferable knowledge from unlabeled graph data to improve downstream performance …