Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

High-throughput experimental methods in neuroscience have led to an explosion of
techniques for measuring complex interactions and multi-dimensional patterns. However …

Message-passing neural networks (MPNNs) have been successfully applied to
representation learning on graphs in a variety of real-world applications. However, two …

In node classification tasks, graph convolutional neural networks (GCNs) have
demonstrated competitive performance over traditional methods on diverse graph data …

Twitter is the third most popular worldwide Online Social Network (OSN) after Facebook and
Instagram. Compared to other OSNs, it has a simple data model and a straightforward data …

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world
applications due to their strong ability in graph representation learning. GNNs explore the …

Multilayer networks is a rising topic in Network Science which characterizes the structure
and the function of complex systems formed by several interacting networks. Multilayer …

从统计物理学来看, 网络是一个包含了大量个体及个体之间相互作用的系统.
本文从统计物理学的角度整理与总结了复杂网络目前的主要研究结果, 并对将来的研究工作做了 …

Microbial networks are an increasingly popular tool to investigate microbial community
structure, as they integrate multiple types of information and may represent systems-level …