Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

Biological networks are powerful resources for the discovery of genes and genetic modules
that drive disease. Fundamental to network analysis is the concept that genes underlying the …

Graph neural network (GNN)'s success in graph classification is closely related to the
Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features …

Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-
structured data. While numerous approaches have been proposed to improve GNNs in …

网络中的链路预测是指如何通过已知的网络结构等信息预测网络中尚未产生连边的两个节点之
间产生连接的可能性. 预测那些已经存在但尚未被发现的连接实际上是一种数据挖掘的过程 …

An intensely debated topic is whether political polarization on social media is on the rise. We
can investigate this question only if we can quantify polarization, by taking into account how …

In applications such as social, energy, transportation, sensor, and neuronal networks, high-
dimensional data naturally reside on the vertices of weighted graphs. The emerging field of …

Networks are finite metric spaces, with distances defined by the shortest paths between
nodes. However, this is not the only form of network geometry: two others are the geometry …

Genetic data often exhibit patterns broadly consistent with'isolation by distance'—a
phenomenon where genetic similarity decays with geographic distance. In a heterogeneous …

Link prediction in complex networks has attracted increasing attention from both physical
and computer science communities. The algorithms can be used to extract missing …